Active airborne noise abatement

ABSTRACT

Noises that are to be emitted by an aerial vehicle during operations may be predicted using one or more machine learning systems, algorithms or techniques. Anti-noises having equal or similar intensities and equal but out-of-phase frequencies may be identified and generated based on the predicted noises, thereby reducing or eliminating the net effect of the noises. The machine learning systems, algorithms or techniques used to predict such noises may be trained using emitted sound pressure levels observed during prior operations of aerial vehicles, as well as environmental conditions, operational characteristics of the aerial vehicles or locations of the aerial vehicles during such prior operations. Anti-noises may be identified and generated based on an overall sound profile of the aerial vehicle, or on individual sounds emitted by the aerial vehicle by discrete sources.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/858,270, filed Sep. 18, 2015, the contents of which are herebyincorporated by reference herein in their entirety.

BACKGROUND

Sound is kinetic energy released by the vibration of molecules in amedium, such as air. In industrial applications, sound may be generatedin any number of ways or in response to any number of events. Forexample, sound may be generated in response to vibrations resulting fromimpacts or frictional contact between two or more bodies. Sound may alsobe generated in response to vibrations resulting from the rotation ofone or more bodies such as shafts, e.g., by motors or other primemovers. Sound may be further generated in response to vibrations causedby fluid flow over one or more bodies. In essence, any movement ofmolecules, or contact between molecules, that causes a vibration mayresult in the emission of sound at a pressure level or intensity, and atone or more frequencies.

The use of unmanned aerial vehicles such as airplanes or helicoptershaving one or more propellers is increasingly common. Such vehicles mayinclude fixed-wing aircraft, or rotary wing aircraft such asquad-copters (e.g., a helicopter having four rotatable propellers),octo-copters (e.g., a helicopter having eight rotatable propellers) orother vertical take-off and landing (or VTOL) aircraft having one ormore propellers. Typically, each of the propellers is powered by one ormore rotating motors or other prime movers.

With their ever-expanding prevalence and use in a growing number ofapplications, unmanned aerial vehicles frequently operate within avicinity of humans or other animals. When an unmanned aerial vehicle iswithin a hearing distance, or earshot, of a human or other animal,noises generated by the unmanned aerial vehicle during operation may bedetected by the human or the other animal. Such noises may include, butare not limited to, sounds generated by rotating propellers, operatingmotors or vibrating frames or structures of the unmanned aerial vehicle.Depending on the sizes of an unmanned aerial vehicle's propellers, theoperational characteristics of its motors or the shapes or dimensions ofits frame or structure, the net effect of the noises generated by theunmanned aerial vehicle may be annoying at best, or deafening at worst.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A through 1D are views of aspects of one system for activeairborne noise abatement in accordance with embodiments of the presentdisclosure.

FIG. 2 is a block diagram of one system for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 3 is a block diagram of one system for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 4 is a flow chart of one process for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 5 is a view of one aerial vehicle configured for active airbornenoise abatement in accordance with embodiments of the presentdisclosure.

FIG. 6 is a view of aspects of one system for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 7 is a flow chart of one process for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 8A and FIG. 8B are views of aspects of one system for activeairborne noise abatement in accordance with embodiments of the presentdisclosure.

FIG. 9 is a flow chart of one process for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 10 is a view of aspects of one system for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 11 is a flow chart of one process for active airborne noiseabatement in accordance with embodiments of the present disclosure.

FIG. 12A and FIG. 12B are views of aspects of one system for activeairborne noise abatement in accordance with embodiments of the presentdisclosure.

DETAILED DESCRIPTION

As is set forth in greater detail below, the present disclosure isdirected to actively abating airborne noise, including but not limitedto noise generated by aerial vehicles during in-flight operations. Morespecifically, the systems and methods disclosed herein are directed toaerial vehicles, such as unmanned aerial vehicles, that are configuredto capture a variety of information or data regarding acoustic energiesthat are generated or encountered during flight, and to correlate theinformation or data regarding the acoustic energies with information ordata regarding the physical or operational environments in which theaerial vehicles were operating when the acoustic energies were generatedor encountered. Such information or data may include, but is not limitedto, extrinsic information or data, e.g., information or data notdirectly relating to the aerial vehicle, or intrinsic information ordata, e.g., information or data relating to the aerial vehicle itself.

For example, extrinsic information or data may include, but is notlimited to, environmental conditions (e.g., temperatures, pressures,humidities, wind speeds and directions), times of day or days of a week,month or year when an aerial vehicle is operating, measures of cloudcoverage, sunshine, or surface conditions or textures (e.g., whethersurfaces are wet, dry, covered with sand or snow or have any othertexture) within a given environment, or any other factors that mayinfluence which acoustic energy is reflected, absorbed, propagated orattenuated within the given environment. Intrinsic information or datamay include, but is not limited to, operational characteristics (e.g.,dynamic attributes such as altitudes, courses, speeds, rates of climb ordescent, turn rates, or accelerations; or physical attributes such asdimensions of structures or frames, numbers of propellers or motors,operating speeds of such motors) or tracked positions (e.g., latitudesand/or longitudes) of the aerial vehicles when the acoustic energies aregenerated or encountered. In accordance with the present disclosure, theamount, the type and the variety of information or data that may becaptured regarding the physical or operational environments in whichaerial vehicles are operating and correlated with information or dataregarding acoustic energies generated or encountered therein istheoretically unbounded.

The extrinsic information or data and/or the intrinsic information ordata captured by aerial vehicles during flight may be used to train amachine learning system to associate an aerial vehicle's operations orlocations, or conditions in such locations, with acoustic energy (e.g.,sound pressure levels or intensities, or frequencies). The trainedmachine learning system, or a sound model developed using such a trainedmachine learned system, may then be used to predict noises that may beexpected when an aerial vehicle operates in a predetermined location, orsubject to a predetermined set of conditions, at given velocities orpositions, or in accordance with any other characteristics. Once suchnoises are predicted, anti-noises, or sounds having substantiallyidentical intensities or pressure levels and frequencies that are whollyout-of-phase with the predicted noises (e.g., having polarities that arereversed with respect to polarities of the predicted noises), may bedetermined, and subsequently emitted from the aerial vehicle duringoperations. When the anti-noises are emitted from one or more sourcesprovided on the aerial vehicle, such anti-noises effectively cancel theeffects of some or all of the predicted noises, thereby reducing oreliminating the sounds heard by humans or other animals within avicinity of the aerial vehicle. In this regard, the systems and methodsof the present disclosure may be utilized to effectively shape theaggregate sounds that are emitted by aerial vehicles during operation,using emitted anti-noises that are intended to counteract the predictednoises.

Referring to FIGS. 1A through 1D, views of aspects of one system 100 foractive airborne noise abatement in accordance with embodiments of thepresent disclosure are shown. As is shown in FIG. 1A, a plurality ofaerial vehicles 110-1, 110-2, 110-3, 110-4 are engaged in flight betweenorigins and destinations. For example, the aerial vehicle 110-1 is shownen route between Hartford, Conn., and Southington, Conn., while theaerial vehicle 110-2 is shown en route between Southport, Conn., andHartford. The aerial vehicle 110-3 is shown en route between Groton,Conn., and Hartford, while the aerial vehicle 110-4 is shown en routebetween Hartford and Storrs, Conn. The aerial vehicles 110-1, 110-2,110-3, 110-4 are configured to capture extrinsic or intrinsicinformation or data 150-1, 150-2, 150-3, 150-4 regarding the aerialvehicles 110-1, 110-2, 110-3, and 110-4 and the environments in whichthe aerial vehicles 110-1, 110-2, 110-3, 110-4 are operating, includingbut not limited to information or data regarding locations, altitudes,courses, speeds, climb or descent rates, turn rates, accelerations, windvelocities, humidity levels and temperatures, using one or more sensors.The aerial vehicles 110-1, 110-2, 110-3, 110-4 are also configured tocapture acoustic information or data regarding noise levels 155-1,155-2, 155-3, 155-4 recorded during their respective flights.

For example, as is shown in the information or data 150-1 of FIG. 1A,the aerial vehicle 110-1 is traveling on a course of 224° and at a speedof 44 miles per hour (mph), in winds of 6 mph out of the northeast, atan altitude of 126 feet, and in air having 50 percent humidity and atemperature of 68 degrees Fahrenheit (° F.). The information or data150-2 of FIG. 1A indicates that the aerial vehicle 110-2 is traveling ona course of 014° and at a speed of 39 mph, in winds of 4 mph out of thesouthwest, at an altitude of 180 feet, and in air having 69 percenthumidity and a temperature of 62° F. The information or data 150-3 ofFIG. 1A indicates that the aerial vehicle 110-3 is traveling on a courseof 082° and at a speed of 38 mph, in winds of 4 mph out of the southsouthwest, at an altitude of 127 feet and in air having 78% humidity anda temperature of 74° F. Finally, the information or data 150-4 of FIG.1A indicates that the aerial vehicle 110-4 is traveling on a course of312° and at a speed of 48 mph, in winds of 8 mph out of the northwest,at an altitude of 151 feet and in air having 96 percent humidity and atemperature of 71° F.

Additionally, the information or data 155-1 indicates that the aerialvehicle 110-1 has recorded noise at a sound pressure level of 88decibels (dB) and at a frequency of 622 Hertz (Hz). The information ordata 155-2 indicates that the aerial vehicle 110-2 has recorded noise ata sound pressure level of 78 dB and at a frequency of 800 Hz, while theinformation or data 155-3 indicates that the aerial vehicle 110-3 hasrecorded noise at a sound pressure level of 80 dB and a frequency of 900Hz, and the information or data 155-4 indicates that the aerial vehicle110-4 has recorded noise at a sound pressure level of 85 dB and afrequency of 974 Hz.

In accordance with the present disclosure, the aerial vehicles 110-1,110-2, 110-3, 110-4 may be configured to provide both the extrinsic andintrinsic information or data 150-1, 150-2, 150-3, 150-4 (e.g.,information or data regarding environmental conditions, operationalcharacteristics or tracked positions of the aerial vehicles 110-1,110-2, 110-3, 110-4), and also the information or data 155-1, 155-2,155-3, 155-4 regarding the acoustic noise recorded during the transitsof the aerial vehicles 110-1, 110-2, 110-3, 110-4, to a data processingsystem. The information or data 150-1, 150-2, 150-3, 150-4 and theinformation or data 155-1, 155-2, 155-3, 155-4 may be provided to thedata processing system either in real time or in near-real time whilethe aerial vehicles 110-1, 110-2, 110-3, 110-4 are in transit, or upontheir arrival at their respective destinations. Referring to FIG. 1B,the extrinsic and intrinsic information or data 150-1, 150-2, 150-3,150-4, e.g., observed environmental signals e(t), is provided to amachine learning system 170 as a set of training inputs, and theinformation or data 155-1, 155-2, 155-3, 155-4, e.g., captured soundsignals s(t), regarding the acoustic noise recorded during the transitsof the aerial vehicles 110-1, 110-2, 110-3, 110-4 is provided to themachine learning system 170 as a set of training outputs.

The machine learning system 170 may be fully trained using a substantialcorpus of observed environmental signals e(t) correlated with capturedsound signals s(t) that are obtained using one or more of the aerialvehicles 110-1, 110-2, 110-3, 110-4, and others, to develop a soundmodel f. After the machine learning system 170 has been trained, and thesound model f has been developed, the machine learning system 170 may beprovided with a set of extrinsic or intrinsic information or data (e.g.,environmental conditions, operational characteristics, or positions)that may be anticipated in an environment in which an aerial vehicle isexpected to operate. In some embodiments, the machine learning system170 may reside and/or be operated on one or more computing devices ormachines provided onboard one or more of the aerial vehicles 110-1,110-2, 110-3, 110-4. The machine learning system 170 may receiveinformation or data regarding the corpus of sound signals observed andthe sound signals captured by the other aerial vehicles 110-1, 110-2,110-3, 110-4, for training purposes and, once trained, the machinelearning system 170 may receive extrinsic or intrinsic information ordata that is actually observed by the aerial vehicle, e.g., in real timeor in near-real time, as inputs and may generate outputs correspondingto predicted sound levels based on the information or data.

In other embodiments, the machine learning system 170 may reside and/orbe operated on one or more centrally located computing devices ormachines. The machine learning system 170 may receive information ordata regarding the corpus of sound signals observed and the soundsignals captured by each of the aerial vehicles 110-1, 110-2, 110-3,110-4 in a fleet for training purposes. Once the machine learning system170 is trained, the machine learning system 170 may be used to programcomputing devices or machines each of the aerial vehicles in a fleetwith a sound model that predicts sounds to be generated or encounteredby the aerial vehicles during operation, e.g., in real time or innear-real time, based on extrinsic or intrinsic information or data thatis actually observed by the respective aerial vehicle. In still otherembodiments, the machine learning system 170 may be programmed toreceive extrinsic or intrinsic information or data from operating aerialvehicles, e.g., via wireless means, as inputs. The machine learningsystem 170 may then generate outputs corresponding to predicted soundlevels based on the information or data and return such predicted levelsto the aerial vehicles.

For example, when variables such as an origin, a destination, a speedand/or a planned altitude for the aerial vehicle 110 (e.g., a transitplan for the aerial vehicle) are known, and where variables such asenvironmental conditions, operational characteristics may be known orestimated, such variables may be provided as inputs to the trainedmachine learning system 170. Subsequently, information or data regardingsounds that may be predicted to be generated or encountered by theaerial vehicle 110 as the aerial vehicle 110 travels from the origin tothe destination within such environmental conditions and according tosuch operational characteristics may be received from the trainedmachine learning system 170 as outputs. From such outputs, anti-noise,e.g., one or more signals that are substantially equal in intensity andopposite in phase to the sounds that may be predicted to be generated orencountered, may be determined in real time or near-real time as theaerial vehicle 110 is en route from the origin to the destination, or inone or more batch processing operations.

Referring to FIG. 1C, an operational input 160 in the form ofenvironmental signals e(t) is provided to the trained machine learningsystem 170, and an operational output 165 in the form of predicted noisesignals s(t) is produced by the sound model f and received from thetrained machine learning system 170. For example, the operational input160 may include extrinsic or intrinsic information or data regarding aplanned transit of an aerial vehicle (e.g., predicted environmental oroperational conditions), or extrinsic or intrinsic information or dataregarding an actual transit of the aerial vehicle (e.g., actuallyobserved or determined environmental or operational conditions),including but not limited to coordinates of an origin, a destination, orof any intervening points, as well as a course and a speed of the aerialvehicle, a wind velocity in a vicinity of the origin, the destination orone or more of the intervening points, an altitude at which the aerialvehicle is expected to travel, and a humidity level and a temperature ina vicinity of the origin, the destination or one or more of theintervening points. The operational output 165 may include informationregarding noises that are expected to be generated or encountered whenthe aerial vehicle operates in a manner consistent with the operationalinput 160, e.g., when the aerial vehicle travels along a similar courseor speed, or at a similar altitude, or encounters a similar windvelocity, humidity level, or temperature.

Based at least in part on the operational output 165 that was determinedbased on the operational input 160, an anti-noise 165′, e.g., a noisehaving a predetermined sound pressure level or intensity, and afrequency that is one hundred eighty degrees out-of-phase, or −s(t),with the operational output 165. In some embodiments, the sound pressurelevel or the intensity of the anti-noise 165′ may be selected tocompletely cancel out or counteract the effects of the noises associatedwith the operational output 165, e.g., such that the sound pressurelevel or the intensity of the anti-noise 165′ equals the sound pressurelevel or intensity of the noises that may be expected to be generated orencountered during operation of the aerial vehicle 110, or of the noisesthat are actually generated or encountered. Alternatively, in someembodiments, the sound pressure level or the intensity of the anti-noise165′ may be selected to partially cancel out or counteract the effectsof noises associated with the operational output 165, e.g., such thatthe sound pressure level or the intensity of the anti-noise 165′ is lessthan the sound pressure level or intensity of the noises that may beexpected during operation of the aerial vehicle 110. Moreover, where theoperational output 165 identifies two or more noises that may beexpected to be generated or encountered by an aerial vehicle based onthe operational input 160, the anti-noise 165′ may include soundpressure levels or intensities and frequencies of each of such noises,and each of such noises may be emitted from the aerial vehicle duringoperations.

Referring to FIG. 1D, an aerial vehicle 110-5 including a plurality ofrotors 113-1, 113-2, 113-3, 113-4 and a plurality of motors 115-1,115-2, 115-3, 115-4 is shown en route from Hartford to Glastonbury,Conn. The aerial vehicle 110-5 is shown as emitting noise consistentwith the operational output 165, and also the anti-noise 165′ from oneor more sound emitting devices 142 (e.g., a speaker). In this regard,the anti-noise 165′ may cancel out noises that are consistent with theoperational output 165 during normal operations.

Accordingly, the systems and methods of the present disclosure may bedirected to actively abating airborne noise, e.g., noises emitted byaerial vehicles during normal operations. Information or data regardingacoustic energy generated by such aerial vehicles during such operationsmay be captured and stored, and provided to one or more machine learningsystems along with extrinsic or intrinsic information or data regardingenvironmental conditions, operational characteristics, or trackedpositions of the aerial vehicles when such acoustic energies wererecorded. The machine learning systems may reside or operate oncomputing devices or machines provided on aerial vehicles or,alternatively, may reside or operate on centrally located or networkedcomputing devices or machines that are accessible to one or more aerialvehicles in a fleet.

The machine learning systems of the present disclosure may operate in anumber of phases or modes. First, in a data capturing phase or mode, amachine learning system, or one or more computing devices or machines onwhich the system resides or operates, captures one or more sets oftraining data during the operation of an aerial vehicle. Such trainingdata may include all available information or data regardingenvironmental conditions and/or operating characteristics of an aerialvehicle, as well as any available information or data regarding soundsor other audio signals that are generated or encountered by the aerialvehicle during flight (e.g., sound pressure levels or intensities andfrequencies of each of such noise). In some embodiments, the trainingdata may further include video imagery or metadata associated with theenvironmental conditions, operating characteristics and/or sounds orother audio signals.

Once the training data has been captured, the machine learning system orthe one or more computing devices or machines may transition to atraining mode, in which the machine learning system is trained based onthe imaging data, e.g., inputs in the form of trained environmentalconditions, operating characteristics, and any other information or dataregarding the operation of an aerial vehicle, such as video imagery ormetadata, and outputs in the form of sounds or other audio signalsgenerated or encountered by the aerial vehicle during flight. In thetraining mode, a sound model, or a function for predicting sounds to begenerated or encountered by the aerial vehicle during operation based onenvironmental conditions and/or operating characteristics or otherinputs, is derived. In some embodiments, the sound model may be trainedto return a predicted sound based on inputs in accordance with theNyquist frequency, e.g., approximately forty kilohertz (40 kHz), or inapproximately twenty-five milliseconds (25 ms).

After the sound model has been derived, the machine learning system mayoperate in a prediction mode. For example, one or more computing devicesor machines provided onboard an aerial vehicle may be configured toreceive inputs from a variety of sources, including but not limited toonboard sensors, and to identify anticipated sounds based on such inputsaccording to the sound model. The inputs may comprise not only actual,determined information or data regarding the environmental conditionsand/or operating characteristics of the aerial vehicle but also anyinformation or data regarding predicted environmental conditions oroperating characteristics of the aerial vehicle.

For example, a sound model may be configured to evaluate not onlyextrinsic or intrinsic information or data regarding the aerial vehiclethat is captured in real time or near-real time from an aerial vehicleduring operations but also predicted extrinsic or intrinsic informationor data regarding such conditions or characteristics that may beanticipated in an area in which the aerial vehicle is operating. In thisregard, the information or data utilized to identify anticipated soundsmay be weighted based on the reliability of the extrinsic or intrinsicinformation or data determined using the onboard sensors (e.g., anextent to which the information or data may be expected to remainconstant), the quality of the predicted extrinsic or intrinsicinformation or data (e.g., a level of confidence in estimates orforecasts on which such information or data is derived), or on any otherfactor. Moreover, predicted extrinsic or intrinsic information or datamay be utilized exclusively to identify anticipated sounds in the eventthat one or more sensors onboard an aerial vehicle malfunctions duringflight, or where an aerial vehicle operates without a full complement ofsensors.

Once a sound model has predicted one or more sounds that may beanticipated by an aerial vehicle during normal operations, e.g., soundpressure levels or intensities and frequencies expected to be generatedor encountered by the aerial vehicle, one or more anti-noises, e.g.,sounds having substantially identical intensities or pressure levels andfrequencies that are out-of-phase with the anticipated sounds, may bedefined and emitted by the aerial vehicle during the normal operations.Moreover, where a machine learning system operates in a prediction mode,with the sound model predicting sounds anticipated by the aerial vehicleduring such operations in real time or in near-real time based on one ormore inputs, the machine learning system may also continue to captureadditional information or data regarding sounds that were actuallygenerated or encountered during such operations. Such additionalinformation or data may be used to further update the machine learningsystem, and to generate a more refined sound model thereby.

When a machine learning system of the present disclosure is successfullytrained to associate acoustic energy with environmental conditions,operational characteristics or tracked positions, e.g., when the machinelearning system has successfully developed a sound model based on acorpus of recorded acoustic energy correlated with such environmentalconditions, operational characteristics or tracked positions,information or data regarding a planned evolution of an aerial vehicle(e.g., a transit plan identifying a route or track along which theaerial vehicle is intended to travel between an origin and adestination, as well as altitudes, courses, speeds, climb or descentrates, turn rates or accelerations required in order to execute thetransit plan, and environmental conditions within a vicinity of theroute or track), or an actual evolution of the aerial vehicle (e.g.,extrinsic or intrinsic information or data regarding the operation ofthe aerial vehicle traveling along the route or track between the originand the destination) may be provided to the trained machine learningsystem, and one or more anti-noises to be emitted while the aerialvehicle is en route may be predicted. The anti-noises may generallyrelate to an overall sound profile of the aerial vehicle, or to soundprofiles of discrete parts or components of the aerial vehicle (e.g., afirst anti-noise directed to addressing noises emitted by rotatingpropellers, a second anti-noise directed to addressing noises emitted byoperating motors, or a third anti-noise directed to addressingvibrations caused by air flowing over a chassis or fuselage). Thus,using historical data regarding operations of the aerial vehicles, andthe environments in which such vehicles are operated, as well asinformation or data regarding such operations or such environmentsdetermined in real time or in near-real time, noises may be activelyabated with predicted anti-noises emitted from one or more components ofthe aerial vehicles.

Sound is generated when motion or vibration of an object results in apressure change in a medium, such as air, surrounding the object. Forexample, when such motion or vibration occurs, the densities of themolecules of the medium within a vicinity of the object are subjected toalternating periods of condensation and rarefaction, resulting incontractions and expansions of such molecules, which causes the issuanceof a sound wave that may travel at speeds of approximately three hundredforty-three meters per second (343 m/s) in dry air. The intensity ofsounds is commonly determined as a sound pressure level (or soundlevel), and is measured in logarithmic units called decibels (dB).

In industrial applications, noise is typically generated as eithermechanical noise, fluid noise or electromagnetic noise. Mechanical noisetypically results when a solid vibrating surface, e.g., a drivensurface, or a surface in contact with one or linkages or prime movers,emits sound power that is a function of a density of a medium, the speedof sound within the medium, the vibrating area, the mean squarevibrating velocity of the medium to a vibrating area and a mean squarevibrating velocity, and the radiation efficiency of the material. Fluidnoise generated by turbulent flow is generally proportional to multipleorders of flow velocity, e.g., six to eight powers greater than thevelocity of the turbulent flow, while sound power generated by rotatingfans is determined according to a function of flow rate and staticpressure. In electric motors, noise may be generated due to airflow atinlets and outlets of cooling fans, bearing or casing vibrations, motorbalancing shaft misalignment or improper motor mountings.

With regard to a frequency spectrum, emitted sounds generally fall intoone of two categories. Sounds having energies that are typicallyconcentrated or centered around discrete frequencies are classified asnarrowband noise, or narrowband tonals, and are commonly periodic innature. Narrowband noise is commonly encountered in many industrialapplications. For example, many rotating machines such as internalcombustion engines, compressors, vacuum pumps or other rotating machinesmay inherently vibrate at frequencies associated with their angularvelocities, as well as electric power transformers that generate largemagnetic fields and thereby vibrate at harmonics of line frequencies.Conversely, sounds having energies that are distributed across bands offrequencies are classified as broadband noise. Additionally, somemachines or sound sources may emit sounds that are combinations ofnarrowband noise and broadband noise, e.g., sounds that have componentenergy levels that are concentrated about one or more discretefrequencies and also across entire frequency spectra.

One primary technique for active noise control or abatement is noisecancellation, in which a cancelling signal of “anti-noise” is generatedelectronically and emitted in the form of sound from transducers. Inthis regard, where anti-noise is substantially equal in amplitude to anarrowband noise centered around a discrete frequency, and is perfectlyout-of-phase (e.g., one hundred eighty degrees out-of-phase, or ofreverse polarity), and emitted in association with the narrowband noise,the anti-noise may effectively address or cancel the narrowband noise.The anti-noise may be determined with regard to narrowband noises thatare cumulative of a plurality of noise sources, e.g., a singleanti-noise emitted with respect to multiple noises, or with regard tonarrowband noises from the plurality of noise sources individually,e.g., multiple anti-noises emitted with respect to one or more of themultiple noises. Alternatively, multiple narrowband anti-noises may beemitted simultaneously to address or cancel the effects of broadbandnoise.

The systems and methods of the present disclosure are directed toactively abating airborne noises, e.g., noises emitted by aerialvehicles. In some embodiments, aerial vehicles may capture informationor data regarding acoustic energies generated or encountered by suchvehicles during normal operations. Some of the acoustic energies mayhave been generated by the aerial vehicles themselves, e.g., noisesemitted by rotating rotors, motors, or air flow over portions of theaerial vehicles, while other acoustic energies may be objective orintrinsic to the environments through which the aerial vehicles traveled(e.g., constant or predictable noises within such environments), andstill other acoustic energies may be subjective or variable based on thetimes or dates on which the aerial vehicles traveled (e.g., weather orother unique events or occurrences on such times or dates).

Once captured, such information or data may be correlated withinformation or data regarding various environmental conditionsencountered (e.g., temperatures, pressures, humidities, wind speeds,directions), operational characteristics (e.g., altitudes, courses,speeds, climb or descent rates, turn rates, accelerations, dimensions ofstructures or frames, numbers of propellers or motors, operating speedsof such motors) or tracked positions (e.g., latitudes and/or longitudes)of such aerial vehicles when the information or data regarding theacoustic energies was captured. The information or data regarding theacoustic conditions and also the environmental conditions, operationalcharacteristics or positions may be captured using one or more onboardsensors, such as microphones, cameras or other imaging devices,piezoelectric monitors, or other like components, and provided to amachine learning system in real time, near-real time, or upon thearrival of the aerial vehicles at their intended destinations, in one ormore synchronous, asynchronous or batch processing techniques. Themachine learning system may reside or be provided on one or morecomputing devices or machines onboard the aerial vehicles themselves, orin another location that may be accessed by the aerial vehicles (e.g.,wirelessly) over one or more networks during operation.

Subsequently, the information or data regarding the environmentalconditions, operational characteristics or tracked positions may beprovided to a machine learning system as training inputs, as well asindependently available information such as times of day, or days of aweek, month or year, and the information or data regarding the acousticenergies encountered may be provided to the machine learning system astraining outputs. The machine learning system may be trained to developa sound model that recognizes associations between the environmentalconditions, operational characteristics and tracked positions and theacoustic energies. Once the machine learning system is sufficientlytrained, information or data regarding an expected or planned transit ofan aerial vehicle may be provided to the trained machine learning systemas an input, and information or data regarding acoustic energies thatare anticipated during the expected or planned transit may be receivedfrom the machine learning system as outputs. For example, where anaerial vehicle is intended to travel from an origin to a destination ona given day and time, information regarding the coordinates of theorigin and the destination, as well as a course or bearing between theorigin and the destination, a projected speed and/or altitude of theaerial vehicle or any projected weather conditions on the day and at thetime may be provided to the trained machine learning system, and ananticipated acoustic energy (e.g., noise level) may be received from thetrained machine learning system. An anti-noise, or one or moreanti-noises, to be emitted by the aerial vehicle during operation may bepredicted by the machine learning system based on the anticipatedacoustic energies. Subsequently, as actual environmental or operationalconditions of the aerial vehicle are determined while the aerial vehicleis en route from the origin to the destination, e.g., from one or moreonboard sensors, such information or data may be provided to the trainedmachine learning system, and the machine learning system may be updatedbased on the information or data accordingly.

Those of ordinary skill in the pertinent arts will recognize that anytype or form of machine learning system (e.g., hardware and/or softwarecomponents or modules) may be utilized in accordance with the presentdisclosure. For example, an emitted noise level may be associated withone or more of an environmental condition, an operating characteristicor a physical location or position of an aerial vehicle according to oneor more machine learning algorithms or techniques, including but notlimited to nearest neighbor methods or analyses, artificial neuralnetworks, conditional random fields, factorization methods ortechniques, K-means clustering analyses or techniques, similaritymeasures such as log likelihood similarities or cosine similarities,latent Dirichlet allocations or other topic models, or latent semanticanalyses. Using any of the foregoing algorithms or techniques, or anyother algorithms or techniques, a relative association between emittedsounds and such environmental conditions, operating characteristics orlocations of aerial vehicles may be determined.

For example, all environmental conditions, operating characteristics orlocations falling within a predefined threshold proximity of one anothermay be placed in or associated with a common cluster or group for agiven intensity or frequency of emitted sound. Such clusters or groupsmay be defined for an entire set of such conditions, characteristics orlocations, or, alternatively, among a subset, or a training set, of suchconditions, characteristics, or locations in the product catalog, andextrapolated among the remaining conditions, characteristics, orlocations. Similarly, clusters or groups of conditions, characteristics,or locations may be defined and associated with emitted sounds based onco-occurrence frequencies, correlation measurements or any otherassociations of the conditions, characteristics, or locations.

In some embodiments, a machine learning system may identify not only asound pressure level or intensity and a frequency of a predicted noisebut also a confidence interval, confidence level or other measure ormetric of a probability or likelihood that the predicted noise will begenerated or encountered by an aerial vehicle in a given environmentthat is subject to given operational characteristics at a givenposition. Where the machine learning system is trained using asufficiently large corpus of recorded environmental signals and soundsignals, and a reliable sound model is developed, the confidenceinterval associated with a sound pressure level or intensity and afrequency of an anti-sound identified thereby may be substantially high.Where the machine learning system is not adequately trained with respectto a given environment, given operational characteristics or a givenposition, however, the confidence interval associated with the soundpressure level or intensity and the frequency may be substantially low.

Moreover, in some embodiments, a machine learning system may identifytwo or more noises or sounds that may be expected to be generated orencountered by an aerial vehicle during operations. In such instances,two or more corresponding anti-noises having corresponding soundpressure levels or intensities and frequencies may also be identified inresponse to the identification of such noises or sounds. For example, insome embodiments, the anti-noises may be independently andsimultaneously emitted during operations, e.g., at the sound pressurelevels or intensities at full strength, from one or more sound emittersprovided on the aerial vehicle. In this regard, sound waves associatedwith the anti-noise signals may constructively or destructivelyinterfere with one another according to wave superposition principles.Alternatively, in some embodiments, the anti-noises may be emittedaccording to a weighted superposition, wherein one of the anti-noisesignals is emitted at a greater sound pressure level or intensity thananother of the anti-noise signals, or at a predetermined weighting orratio with respect to other various anti-noise signals.

In accordance with the present disclosure, the extent of extrinsic orintrinsic information or data that may be captured regarding acousticenergy generated or encountered by an aerial vehicle or theenvironmental conditions, operational characteristics or positions ofthe aerial vehicle, and subsequently stored and evaluated, is notlimited. For example, where a fleet of aerial vehicles operates in agiven area on a regular basis, e.g., at varying times of a day, days ofa week, or weeks, months or seasons of a year, vast sums of informationor data regarding acoustic energies generated or encountered by suchvehicles during operation may be captured and provided to a machinelearning system, and the machine learning system may repeatedly trainand retrain itself as new information or data becomes available. As aresult, a sound model produced as a result of the training of themachine learning system is continuously refined, and the quality of thepredictions of acoustic energies identified thereby is improved.Furthermore, aerial vehicles may be directed to either capturinginformation or data that may be used to identify anti-noises, or to emitanti-noises based on previously captured information or data.Alternatively, an aerial vehicle may both emit anti-noises based onpreviously captured information or data while also capturing informationor data to be used to further improve predictions of generated orencountered noises, and the subsequent generation of anti-noises, in thefuture, thereby continuing to refine the process by which noises arepredicted, and anti-noises are generated.

Moreover, although one variable that may be associated with acousticenergies encountered by an aerial vehicle is a position of the aerialvehicle (e.g., a latitude or longitude), and that extrinsic or intrinsicinformation or data associated with the position may be used to predictacoustic energies generated or encountered by the aerial vehicle at thatposition, those of ordinary skill in the pertinent arts will recognizethat the systems and methods of the present disclosure are not solimited. Rather, acoustic energies may be predicted for areas orlocations having similar environmental conditions or requiring aerialvehicles to exercise similar operational characteristics. For example,because environmental conditions in Vancouver, British Columbia, and inLondon, England, are known to be generally similar to one another,information or data gathered regarding the acoustic energies generatedor encountered by aerial vehicles operating in the Vancouver area may beused to predict acoustic energies that may be generated or encounteredby aerial vehicles operating in the London area, or to generateanti-noise signals to be emitted by aerial vehicles operating in theLondon area. Likewise, information or data gathered regarding theacoustic energies generated or encountered by aerial vehicles operatingin the London area may be used to predict acoustic energies that may begenerated or encountered by aerial vehicles operating in the Vancouverarea, or to generate anti-noise signals to be emitted by aerial vehiclesoperating in the Vancouver area.

Those of ordinary skill in the pertinent arts will recognize thatanti-noise may be emitted from any type of sound emitting device inaccordance with the present disclosure. For example, where noise isanticipated at a given intensity and frequency, anti-noise of the sameor a similar intensity may be emitted at the frequency, one hundredeighty degrees out-of-phase or of reverse polarity, from not only atraditional audio speaker but also from other devices such aspiezoelectric components that are configured to vibrate at givenresonant frequencies upon being energized or excited by an electricsource.

Additionally, those of ordinary skill in the pertinent arts will furtherrecognize that anti-noise may be emitted constantly, e.g., throughout anentire duration of a transit by an aerial vehicle, or at particularintervals or in specific locations that are selected based on one ormore intrinsic or extrinsic requirements. For example, where an aerialvehicle is operating out of earshot of any human or other animal, e.g.,in locations where no such humans are expected to be located, such asover water, deserts, or ice, anti-noise need not be emitted, and batterylevels or other onboard electric power may be conserved. Similarly,where an aerial vehicle is operating in a location where the noiseemitted by the aerial vehicle is comparatively irrelevant, e.g., wherethe noise emitted by the aerial vehicle is dwarfed by other ambientnoise, anti-noise need not be emitted. Furthermore, anti-noise may, butneed not, account for all noise emitted by an aerial vehicle duringoperation. For example, anti-noise may be emitted at equal intensitylevels to noise encountered by an aerial vehicle, and at frequenciesthat are one hundred eighty degrees out-of-phase, or of reversepolarity, with the intent of eliminating or reducing the effects of suchnoise to the maximum extent practicable. Alternatively, anti-noise maybe emitted at intensity levels that are less than the intensity of thenoise encountered by the aerial vehicle, and may be intended to reducethe effects of such noise to within allowable specifications orstandards.

Moreover, in accordance with the present disclosure, a trained machinelearning system may be used to develop sound profiles for aerialvehicles based on their sizes, shapes, or configurations, and withrespect to environmental conditions, operational characteristics, orlocations of such aerial vehicles. Based on such sound profiles,anti-noise levels may be determined for such aerial vehicles as afunction of the respective environmental conditions, operationalcharacteristics or locations and emitted on an as-needed basis.Alternatively, the trained machine learning system may be used todevelop sound profiles for individual, particular aspects of an aerialvehicle. For example, a sound profile may be determined for a rotor orpropeller of a given size (e.g., diameter), number of blades, or otherattributes, or for a motor having a given power level, capacity oroperational speed, or an airframe of given dimensions, sizes or shapes.Where aspects of aerial vehicles are interchangeable with one another,e.g., where a given rotor or motor may be utilized on different aerialvehicle airframes, an overall sound profile for the aerial vehicle maybe constructed from the individual sound profiles of the respectiveaspects. Anti-noise levels may be determined for and emitted by anaerial vehicle based on an overall sound profile of the aerial vehicle,or the individual sound profiles of the respective parts thereof, inaccordance with the present disclosure.

Referring to FIG. 2, a block diagram of components of one system 200 foractive airborne noise abatement in accordance with embodiments of thepresent disclosure. The system 200 of FIG. 2 includes an aerial vehicle210 and a data processing system 270 connected to one another over anetwork 280. Except where otherwise noted, reference numerals precededby the number “2” shown in the block diagram of FIG. 2 indicatecomponents or features that are similar to components or features havingreference numerals preceded by the number “1” shown in the system 100 ofFIGS. 1A through 1D.

The aerial vehicle 210 includes a processor 212, a memory or storagecomponent 214 and a transceiver 216, as well as a plurality ofenvironmental or operational sensors 220, a plurality of sound sensors230 and a plurality of sound emitters 240.

The processor 212 may be configured to perform any type or form ofcomputing function, including but not limited to the execution of one ormore machine learning algorithms or techniques. For example, theprocessor 212 may control any aspects of the operation of the aerialvehicle 210 and the one or more computer-based components thereon,including but not limited to the transceiver 216, the environmental oroperational sensors 220, the sound sensors 230, and/or the soundemitters 240. The aerial vehicle 210 may likewise include one or morecontrol systems (not shown) that may generate instructions forconducting operations thereof, e.g., for operating one or more rotors,motors, rudders, ailerons, flaps or other components provided thereon.Such control systems may be associated with one or more other computingdevices or machines, and may communicate with the data processing system270 or one or more other computer devices (not shown) over the network280, through the sending and receiving of digital data. The aerialvehicle 210 further includes one or more memory or storage components214 for storing any type of information or data, e.g., instructions foroperating the aerial vehicle, or information or data captured by one ormore of the environmental or operational sensors 220, the sound sensors230, and/or the sound emitters 240.

The transceiver 216 may be configured to enable the aerial vehicle 210to communicate through one or more wired or wireless means, e.g., wiredtechnologies such as Universal Serial Bus (or “USB”) or fiber opticcable, or standard wireless protocols such as Bluetooth® or any WirelessFidelity (or “WiFi”) protocol, such as over the network 280 or directly.

The environmental or operational sensors 220 may include any componentsor features for determining one or more attributes of an environment inwhich the aerial vehicle 210 is operating, or may be expected tooperate, including extrinsic information or data or intrinsicinformation or data. As is shown in FIG. 2, the environmental oroperational sensors 220 may include, but are not limited to, a GlobalPositioning System (“GPS”) receiver or sensor 221, a compass 222, aspeedometer 223, an altimeter 224, a thermometer 225, a barometer 226, ahygrometer 227, or a gyroscope 228. The GPS sensor 221 may be anydevice, component, system or instrument adapted to receive signals(e.g., trilateration data or information) relating to a position of theaerial vehicle 210 from one or more GPS satellites of a GPS network (notshown). The compass 222 may be any device, component, system, orinstrument adapted to determine one or more directions with respect to aframe of reference that is fixed with respect to the surface of theEarth (e.g., a pole thereof). The speedometer 223 may be any device,component, system, or instrument for determining a speed or velocity ofthe aerial vehicle 210, and may include related components (not shown)such as pitot tubes, accelerometers, or other features for determiningspeeds, velocities, or accelerations.

The altimeter 224 may be any device, component, system, or instrumentfor determining an altitude of the aerial vehicle 210, and may includeany number of barometers, transmitters, receivers, range finders (e.g.,laser or radar) or other features for determining heights. Thethermometer 225, the barometer 226 and the hygrometer 227 may be anydevices, components, systems, or instruments for determining local airtemperatures, atmospheric pressures, or humidities within a vicinity ofthe aerial vehicle 210. The gyroscope 228 may be any mechanical orelectrical device, component, system, or instrument for determining anorientation, e.g., the orientation of the aerial vehicle 210. Forexample, the gyroscope 228 may be a traditional mechanical gyroscopehaving at least a pair of gimbals and a flywheel or rotor.Alternatively, the gyroscope 228 may be an electrical component such adynamically tuned gyroscope, a fiber optic gyroscope, a hemisphericalresonator gyroscope, a London moment gyroscope, a microelectromechanicalsensor gyroscope, a ring laser gyroscope, or a vibrating structuregyroscope, or any other type or form of electrical component fordetermining an orientation of the aerial vehicle 210.

Those of ordinary skill in the pertinent arts will recognize that theenvironmental or operational sensors 220 may include any type or form ofdevice or component for determining an environmental condition within avicinity of the aerial vehicle 210 in accordance with the presentdisclosure. For example, the environmental or operational sensors 220may include one or more air monitoring sensors (e.g., oxygen, ozone,hydrogen, carbon monoxide or carbon dioxide sensors), infrared sensors,ozone monitors, pH sensors, magnetic anomaly detectors, metal detectors,radiation sensors (e.g., Geiger counters, neutron detectors, alphadetectors), attitude indicators, depth gauges, accelerometers or thelike, as well as one or more imaging devices (e.g., digital cameras),and are not limited to the sensors 221, 222, 223, 224, 225, 226, 227,228 shown in FIG. 2.

The sound sensors 230 may include other components or features fordetecting and capturing sound energy in a vicinity of an environment inwhich the aerial vehicle 210 is operating, or may be expected tooperate. As is shown in FIG. 2, the sound sensors 230 may include amicrophone 232, a piezoelectric sensor 234, and a vibration sensor 236.The microphone 232 may be any type or form of transducer (e.g., adynamic microphone, a condenser microphone, a ribbon microphone, acrystal microphone) configured to convert acoustic energy of anyintensity and across any or all frequencies into one or more electricalsignals, and may include any number of diaphragms, magnets, coils,plates, or other like features for detecting and recording such energy.The microphone 232 may also be provided as a discrete component, or incombination with one or more other components, e.g., an imaging devicesuch as a digital camera. Furthermore, the microphone 232 may beconfigured to detect and record acoustic energy from any and alldirections.

The piezoelectric sensor 234 may be configured to convert changes inpressure, including but not limited to such pressure changes that areinitiated by the presence of acoustic energy across various bands offrequencies, to electrical signals, and may include one or morecrystals, electrodes or other features. The vibration sensor 236 may beany device configured to detect vibrations of one or more components ofthe aerial vehicle 210, and may also be a piezoelectric device. Forexample, the vibration sensor 236 may include one or moreaccelerometers, e.g., an application-specific integrated circuit and oneor more microelectromechanical sensors in a land grid array package,that are configured to sense differential accelerations along one ormore axes over predetermined periods of time and to associate suchaccelerations with levels of vibration and, therefore, sound.

The sound emitters 240 may further include other components or featuresmounted to or provided on the aerial vehicle 210 for emitting soundsignals at any intensity or at one or more frequencies. As is shown inFIG. 2, the sound emitters 240 may include one or more speakers 242, apiezoelectric emitter 244, or a vibration emitter 246. The speaker 242may be any type or form of transducer for converting electrical signalsinto sound energy. The speaker 242 may have any degree of technicalcomplexity, and may be, for example, an electrodynamic speaker, anelectrostatic speaker, a flat-diaphragm speaker, a magnetostaticspeaker, a magnetostrictive speaker, a ribbon-driven speaker, a planarspeaker, a plasma arc speaker, or any other type or form of speaker.Alternatively, the speaker 242 may be basic or primitive, such as a PCspeaker, e.g., an audio speaker having a limited bit range or capacity.Additionally, the speaker 242 may be a single speaker adapted to emitsounds over a wide range of frequency, or may include one or morecomponents (e.g., tweeters, mid-ranges, and woofers) for emitting soundsover wide ranges of frequencies. A piezoelectric emitter 244 may be asound emitter having an expanding or contracting crystal that vibratesin air or another medium in order to produce sounds. In someembodiments, the piezoelectric emitter 244 may also be the piezoelectricsensor 234. A vibration emitter 246 may be any type or form of deviceconfigured to cause one or more elements of the aerial vehicle 210 tovibrate at a predetermined resonance frequency.

The data processing system 270 includes one or more physical computerservers 272 having one or more computer processors 274 and a pluralityof data stores 276 associated therewith, which may be provided for anyspecific or general purpose. For example, the data processing system 270of FIG. 2 may be independently provided for the exclusive purpose ofreceiving, analyzing or storing acoustic signals or other information ordata received from the aerial vehicle 210 or, alternatively, provided inconnection with one or more physical or virtual services configured toreceive, analyze or store such acoustic signals, information or data, aswell as one or more other functions. The servers 272 may be connected toor otherwise communicate with the processors 274 and the data stores276. The data stores 276 may store any type of information or data,including but not limited to acoustic signals, information or datarelating to acoustic signals, or information or data regardingenvironmental conditions, operational characteristics, or positions, forany purpose. The servers 272 and/or the computer processors 274 may alsoconnect to or otherwise communicate with the network 280, as indicatedby line 278, through the sending and receiving of digital data. Forexample, the data processing system 270 may include any facilities,stations or locations having the ability or capacity to receive andstore information or data, such as media files, in one or more datastores, e.g., media files received from the aerial vehicle 210, or fromone another, or from one or more other external computer systems (notshown) via the network 280. In some embodiments, the data processingsystem 270 may be provided in a physical location. In other suchembodiments, the data processing system 270 may be provided in one ormore alternate or virtual locations, e.g., in a “cloud”-basedenvironment. In still other embodiments, the data processing system 270may be provided onboard one or more aerial vehicles, including but notlimited to the aerial vehicle 210.

The network 280 may be any wired network, wireless network, orcombination thereof, and may comprise the Internet in whole or in part.In addition, the network 280 may be a personal area network, local areanetwork, wide area network, cable network, satellite network, cellulartelephone network, or combination thereof. The network 280 may also be apublicly accessible network of linked networks, possibly operated byvarious distinct parties, such as the Internet. In some embodiments, thenetwork 280 may be a private or semi-private network, such as acorporate or university intranet. The network 280 may include one ormore wireless networks, such as a Global System for MobileCommunications (GSM) network, a Code Division Multiple Access (CDMA)network, a Long Term Evolution (LTE) network, or some other type ofwireless network. Protocols and components for communicating via theInternet or any of the other aforementioned types of communicationnetworks are well known to those skilled in the art of computercommunications and thus, need not be described in more detail herein.

The computers, servers, devices and the like described herein have thenecessary electronics, software, memory, storage, databases, firmware,logic/state machines, microprocessors, communication links, displays orother visual or audio user interfaces, printing devices, and any otherinput/output interfaces to provide any of the functions or servicesdescribed herein and/or achieve the results described herein. Also,those of ordinary skill in the pertinent art will recognize that usersof such computers, servers, devices and the like may operate a keyboard,keypad, mouse, stylus, touch screen, or other device (not shown) ormethod to interact with the computers, servers, devices and the like, orto “select” an item, link, node, hub or any other aspect of the presentdisclosure.

The aerial vehicle 210 or the data processing system 270 may use anyweb-enabled or Internet applications or features, or any otherclient-server applications or features including E-mail or othermessaging techniques, to connect to the network 280, or to communicatewith one another, such as through short or multimedia messaging service(SMS or MMS) text messages. For example, the aerial vehicle 210 may beadapted to transmit information or data in the form of synchronous orasynchronous messages to the data processing system 270 or to any othercomputer device in real time or in near-real time, or in one or moreoffline processes, via the network 280. Those of ordinary skill in thepertinent art would recognize that the aerial vehicle 210 or the dataprocessing system 270 may operate any of a number of computing devicesthat are capable of communicating over the network, including but notlimited to set-top boxes, personal digital assistants, digital mediaplayers, web pads, laptop computers, desktop computers, electronic bookreaders, and the like. The protocols and components for providingcommunication between such devices are well known to those skilled inthe art of computer communications and need not be described in moredetail herein.

The data and/or computer executable instructions, programs, firmware,software and the like (also referred to herein as “computer executable”components) described herein may be stored on a computer-readable mediumthat is within or accessible by computers or computer components such asthe processor 212 or the processor 274, or any other computers orcontrol systems utilized by the aerial vehicle 210 or the dataprocessing system 270, and having sequences of instructions which, whenexecuted by a processor (e.g., a central processing unit, or “CPU”),cause the processor to perform all or a portion of the functions,services and/or methods described herein. Such computer executableinstructions, programs, software, and the like may be loaded into thememory of one or more computers using a drive mechanism associated withthe computer readable medium, such as a floppy drive, CD-ROM drive,DVD-ROM drive, network interface, or the like, or via externalconnections.

Some embodiments of the systems and methods of the present disclosuremay also be provided as a computer-executable program product includinga non-transitory machine-readable storage medium having stored thereoninstructions (in compressed or uncompressed form) that may be used toprogram a computer (or other electronic device) to perform processes ormethods described herein. The machine-readable storage media of thepresent disclosure may include, but is not limited to, hard drives,floppy diskettes, optical disks, CD-ROMs, DVDs, ROMs, RAMs, erasableprogrammable ROMs (“EPROM”), electrically erasable programmable ROMs(“EEPROM”), flash memory, magnetic or optical cards, solid-state memorydevices, or other types of media/machine-readable medium that may besuitable for storing electronic instructions. Further, embodiments mayalso be provided as a computer executable program product that includesa transitory machine-readable signal (in compressed or uncompressedform). Examples of machine-readable signals, whether modulated using acarrier or not, may include, but are not limited to, signals that acomputer system or machine hosting or running a computer program can beconfigured to access, or including signals that may be downloadedthrough the Internet or other networks.

As is discussed above, information or data regarding not only acousticenergies but also environmental conditions, operational characteristicsor positions may be received from any number of aerial vehicles, andsubsequently provided to a data processing system for evaluation andanalysis according to one or more machine learning algorithms ortechniques. Referring to FIG. 3, a block diagram of components of onesystem 300 for active airborne noise abatement in accordance withembodiments of the present disclosure. The system 300 of FIG. 3 includesn aerial vehicles 310-1, 310-2 . . . 310-n and a data processing system370 connected to one another over a network 380. Except where otherwisenoted, reference numerals preceded by the number “3” shown in the blockdiagram of FIG. 3 indicate components or features that are similar tocomponents or features having reference numerals preceded by the number“2” shown in the block diagram of FIG. 2 or by the number “1” shown inthe system of FIGS. 1A through 1D.

As is shown in FIG. 3, the system 300 includes a plurality of n aerialvehicles 310-1, 310-2 . . . 310-n, each having one or more environmentalor operational sensors 320-1, 320-2 . . . 320-n, sound sensors 330-1,330-2 . . . 330-n and sound emitters 340-1, 340-2 . . . 340-n. Thus, inoperation, each of the aerial vehicles 310-1, 310-2 . . . 310-n may beconfigured to capture information or data regarding their environmentalconditions, operational characteristics or positions, as well asacoustic energies encountered by such aerial vehicles 310-1, 310-2 . . .310-n, using one or more of the environmental or operational sensors320-1, 320-2 . . . 320-n or the sound sensors 330-1, 330-2 . . . 330-nand to transmit such information to the data processing system 370 overthe network 380. Each of the aerial vehicles 310-1, 310-2 . . . 310-nmay further include one or more other computer components (not shown),such as one or more of the processors 212, the memory components 214 orthe transceivers 216 of the aerial vehicle 210 shown in FIG. 2.

The data processing system 370 may operate one or more machine learningsystems (e.g., algorithms or techniques) for associating the informationor data captured using the environmental or operational sensors 320-1,320-2 . . . 320-n of the various aerial vehicles 310-1, 310-2 . . .310-n with the noises captured using the sound sensors 330-1, 330-2 . .. 330-n. Likewise, each of the aerial vehicles 310-1, 310-2 . . . 310-nmay be configured to emit anti-noises identified by the data processingsystem 370 using one or more of the sound emitters 340-1, 340-2 . . .340-n. Machine learning systems operated by the data processing system370 may thus be trained or refined in real time, or in near-real time,based on information or data captured by the aerial vehicles 310-1,310-2 . . . 310-n. In some embodiments, such machine learning systemsmay also provide information regarding predicted noises that may begenerated or encountered, and anti-noises for counteracting the effectsof one or more of the predicted noises, to one or more of the aerialvehicles 310-1, 310-2 . . . 310-n, also in real time or in near-realtime.

As is discussed above, in some embodiments, the data processing system370 may be provided a physical location, or in one or more alternate orvirtual locations, e.g., in a “cloud”-based environment. In still otherembodiments, the data processing system 370 may be provided onboard oneor more of the aerial vehicles 310-1, 310-2 . . . 310-n. For example,one or more of the aerial vehicles 310-1, 310-2 . . . 310-n may beconfigured to autonomously capture data on behalf of a machine learningsystem operating thereon, train the machine learning system, e.g., todefine a sound model thereon, and to predict sound pressure levels orintensities and frequencies that are expected to be generated orencountered by one or more of the aerial vehicles 310-1, 310-2 . . .310-n, as well as to identify and emit one or more anti-noises, e.g.,sounds having substantially identical intensities or pressure levels andfrequencies that are out-of-phase with the anticipated sounds.

Alternatively, in some other embodiments, at least one of the one ormore of the aerial vehicles 310-1, 310-2 . . . 310-n may be designatedas a “master” aerial vehicle for the purpose of predicting soundpressure levels or intensities and frequencies that are expected to begenerated or encountered by each of the other aerial vehicles 310-1,310-2 . . . 310-n during operation, and may communicate information ordata regarding the predicted sound pressure levels or intensities andfrequencies to be generated or encountered thereby to the one or more ofthe aerial vehicles 310-1, 310-2 . . . 310-n.

Referring to FIG. 4, a flow chart 400 of one process for active airbornenoise abatement in accordance with embodiments of the present disclosureis shown. At box 410, an aerial vehicle departs from an origin for adestination. The aerial vehicle may be manually instructed orautomatically programmed to travel to the destination for any purpose,including but not limited to delivering an item from the origin to thedestination. Alternatively, the aerial vehicle may travel to thedestination for the express purpose of capturing information or dataregarding environmental conditions, operational characteristics, oracoustic energies at the origin, the destination, or at any interveningwaypoints, and correlating such environmental conditions, operationalcharacteristics or acoustic energies with locations of the aerialvehicle.

At box 420, one or more sensors onboard the aerial vehicle track itsposition during the transit between the origin and the destination. Forexample, the aerial vehicle may include one or more GPS sensors,gyroscopes, accelerometers or other components for tracking a locationof the aerial vehicle in two-dimensional or three-dimensional spacewhile the vehicle is en route from the origin to the destination. Thepositions of the aerial vehicle may be determined continuously, atvarious intervals of time, based on altitudes, courses, speeds, climb ordescent rates, turn rates, or accelerations, or on any other basis. Atbox 430, one or more other sensors determine one or more environmentalconditions encountered by the aerial vehicle, e.g., temperatures,barometric pressures, humidities, wind speeds, or levels ofprecipitation, while at box 440, one or more other sensors determineoperational characteristics of the aerial vehicle while the aerialvehicle is in transit, e.g., motor rotating speeds, propeller rotatingspeeds, altitudes, courses, speeds, climb or descent rates, turn ratesor accelerations of the aerial vehicle. At box 450, one or more otheronboard sensors (e.g., one or more microphones or other sound sensors)determine emitted sound pressure levels and/or frequencies during thetransit of the aerial vehicle between the origin and the destination.

At box 460, the aerial vehicle arrives at the destination. At box 470,the tracked positions of the aerial vehicle are correlated with dataregarding the environmental conditions, the operational characteristics,or the emitted sound pressure levels. For example, when the positions ofthe aerial vehicle are captured, e.g., at box 420, and when informationor data regarding such environmental conditions, operationalcharacteristics or emitted sound pressure levels are captured, e.g., atboxes 430, 440 and 450, the information or data may be time-stamped ormarked with one or more identifiers, and subsequently correlated basedon the times at which the various information or data was captured.Alternatively, the information or data regarding such environmentalconditions, operational characteristics or emitted sound pressure levelsmay be stamped or marked with position information (e.g., latitudes orlongitudes) as the information or data is captured.

At box 480, a machine learning system is trained using data regardingthe environmental conditions, operational characteristics, trackedpositions as training inputs, and the emitted sound pressure levelsand/or frequencies as training outputs, and the process ends. Forexample, the machine learning system may be trained to associate suchdata with emitted sound pressure levels according to any manual orautomatic means, including one or more machine algorithms or techniquessuch as nearest neighbor methods or analyses, factorization methods ortechniques, K-means clustering analyses or techniques, similaritymeasures such as log likelihood similarities or cosine similarities,latent Dirichlet allocations or other topic models, or latent semanticanalyses. The machine learning system may thus result in a sound modelconfigured to identify a predicted noise, e.g., a sound pressure levelor intensity and frequency of a sound that may be expected to begenerated or encountered during the operation of an aerial vehicle in agiven environmental conditions, at given operating characteristics or atgiven positions. The machine learning system may be further trained todetermine confidence levels, probabilities, or likelihoods that thesound pressure level or intensity and frequency will be generated orencountered within such environmental conditions or operationalcharacteristic, or at the tracked positions. In some embodiments, themachine learning system may reside and/or be operated on one or morecentrally located computing devices or machines, or in alternate orvirtual locations, e.g., a “cloud”-based environment. In some otherembodiments, the machine learning system being trained may reside and/orbe operated on one or more computing devices or machines providedonboard one or more aerial vehicles from which the data regarding theenvironmental conditions or the operational characteristics werecaptured and on which the emitted sound pressure levels and/orfrequencies were determined.

Aerial vehicles may include any number of environmental or operationalsensors, noise sensors, noise emitters, and other components forcapturing extrinsic or intrinsic information or data in accordance withthe present disclosure. Referring to FIG. 5, a view of one aerialvehicle 510 configured for active airborne noise abatement in accordancewith embodiments of the present disclosure is shown. Except whereotherwise noted, reference numerals preceded by the number “5” shown inFIG. 5 indicate components or features that are similar to components orfeatures having reference numerals preceded by the number “3” shown inFIG. 3, by the number “2” shown in FIG. 2 or by the number “1” shown inFIGS. 1A through 1D.

The aerial vehicle 510 is an octo-copter including eight motors 513-1,513-2, 513-3, 513-4, 513-5, 513-6, 513-7, and 513-8 and eight propellers515-1, 515-2, 515-3, 515-4, 515-5, 515-6, 515-7, and 515-8. The aerialvehicle 510 also includes a plurality of environmental sensors 520,e.g., sensors of position, orientation, speed, altitude, temperature,pressure, humidity or other conditions or attributes (not shown). Theaerial vehicle 510 further includes sensors for detecting emitted soundpressure levels onboard the aerial vehicle 510, including fourmicrophones 532-1, 532-2, 532-3, 532-4 mounted to an airframe of theaerial vehicle 510, and four piezoelectric sensors 534-1, 534-2, 534-3,534-4 provided at intersections of components of the airframe, e.g., fordetecting vibration of the airframe during operations. The aerialvehicle 510 also includes devices for emitting sounds such as a pair ofspeakers 542-1, 542-2 provided on either side of the aerial vehicle 510,and eight piezoelectric sound emitters 544-1, 544-2, 544-3, 544-4,544-5, 544-6, 544-7, 544-8 mounted to components of the airframe. Theaerial vehicle 510 may further include additional sound emitting devices(not shown), e.g., PC speakers, provided in discrete locations on theaerial vehicle 510. The speakers 542-1, 542-2, the sound emitters 544-1,544-2, 544-3, 544-4, 544-5, 544-6, 544-7, 544-8 or any othersound-emitting components may be configured to emit anti-noise based onnoise that may be predicted to be encountered by the aerial vehicle 510.

As is discussed above, information or data regarding environmentalconditions, operational characteristics or positions determined during atransit of an aerial vehicle, and emitted sound pressure levels recordedduring the transit of the aerial vehicle, may be captured and providedto a machine learning system in real time or in near-real time duringthe transit, at regular or irregular intervals (e.g., over a wired orwireless network connection), or when the transit is complete. Referringto FIG. 6, a view of aspects of one system 600 for active airborne noiseabatement in accordance with embodiments of the present disclosure isshown. Except where otherwise noted, reference numerals preceded by thenumber “6” shown in FIG. 6 indicate components or features that aresimilar to components or features having reference numerals preceded bythe number “5” shown in FIG. 5, by the number “3” shown in FIG. 3, bythe number “2” shown in FIG. 2 or by the number “1” shown in FIGS. 1Athrough 1D.

As is shown in FIG. 6, an aerial vehicle 610 is shown en route betweenBoston, Mass., and Chatham, Mass. At regular intervals of time orposition, e.g., prior to departure from the origin, at times t1, t2, t3,t4 while in transit, and upon an arrival at the destination, informationor data 650-0, 650-1, 650-2, 650-3, 650-4, 650-5 regarding the operationof the aerial vehicle 610 or the environmental conditions in which theaerial vehicle 610 operates, e.g., locations, altitudes, courses,speeds, climb or descent rates, turn rates, accelerations, windvelocities, humidity levels or temperatures, may be captured and storedand/or transmitted to a machine learning system 670 (e.g., upon anarrival of the aerial vehicle 610 at the destination). Likewise,information or data regarding noise levels 655-0-, 655-1, 655-2, 655-3,655-4, 655-5 captured by sensors onboard the aerial vehicle 610 may alsobe stored or transmitted to the machine learning system 670.Subsequently, the machine learning system 670 may be trained using theinformation or data 650-0, 650-1, 650-2, 650-3, 650-4, 650-5 as traininginputs, and the noise levels 655-0-, 655-1, 655-2, 655-3, 655-4, 655-5as training outputs, to recognize and associate environmentalconditions, operational characteristics or positions with emitted soundpressure levels. The machine learning system 670 may further be trainedto determine a confidence interval (or a confidence level or anothermeasure or metric of a probability or likelihood) that emitted soundpressure levels will be generated or encountered by an aerial vehicle ina given environment that is subject to given operational characteristicsat a given position.

Thereafter, when information regarding a planned transit of an aerialvehicle, e.g., the aerial vehicle 610 or another aerial vehicle havingone or more attributes in common with the aerial vehicle 610, isdetermined, such information or data may be provided to the trainedmachine learning system 670 as an input, and an emitted sound pressurelevel or intensity and a frequency anticipated during the plannedtransit may be determined based on an output from the trained machinelearning system 670. Additionally, as is discussed above, a confidenceinterval may be determined and associated with the emitted soundpressure level or intensity and the frequency. An anti-noise to beemitted by the aerial vehicle 610, e.g., continuously during thetransit, or at various intervals, may be determined based on theanticipated emitted sound pressure level or intensity and frequency.Moreover, during the actual transit of the aerial vehicle, informationor data regarding actual environmental conditions, operatingcharacteristics and/or acoustic energies may be captured in real time ornear-real time and utilized to determine one or more anti-noises to beemitted by the aerial vehicle 610 in transit, e.g., using a sound modeltrained to return a predicted sound based on inputs in accordance withthe Nyquist frequency.

Referring to FIG. 7, a flow chart 700 of one process for active airbornenoise abatement in accordance with embodiments of the present disclosureis shown. At box 710, a destination of an aerial vehicle is determined,and at box 720, a transit plan for the aerial vehicle for a transit ofthe aerial vehicle from an origin to the destination is identified. Forexample, the transit plan may specify an estimated time of departurefrom the origin, locations of any waypoints between the origin or thedestination, a desired time of arrival at the destination, or any otherrelevant geographic or time constraints associated with the transit. Atbox 722, operational characteristics of the aerial vehicle that arerequired in order to complete the transit from the origin to thedestination in accordance with the transit plan, e.g., courses or speedsof the aerial vehicle, and corresponding instructions to be provided tosuch motors, rotors, rudders, ailerons, flaps or other features of theaerial vehicle in order to achieve such courses or speeds, may bepredicted. At box 724, environmental conditions may be expected to beencountered during the transit from the origin to the destination inaccordance with the transit plan are predicted. For example, weatherforecasts for the times or dates of the departure or the arrival of theaerial vehicle, and for the locations of the origin or the destination,may be identified on any basis.

At box 726, the transit plan identified at box 720, the predictedoperational characteristics determined at box 722 and the predictedenvironmental conditions predicted at box 724 are provided to a trainedmachine learning system as initial inputs. The machine learning systemmay utilize one or more algorithms or techniques such as nearestneighbor methods or analyses, factorization methods or techniques,K-means clustering analyses or techniques, similarity measures such aslog likelihood similarities or cosine similarities, latent Dirichletallocations or other topic models, or latent semantic analyses, and maybe trained to associate environmental, operational or location-basedinformation with emitted sound pressure levels. In some embodiments, thetrained machine learning system resides and/or operates on one or morecomputing devices or machines provided onboard the aerial vehicle. Insome other embodiments, the trained machine learning system resides inone or more alternate or virtual locations, e.g., in a “cloud”-basedenvironment accessible via a network.

At box 730, one or more predicted sound pressure levels or frequenciesare received from the machine learning system as outputs. Such soundpressure levels or frequencies may be average or general sound pressurelevels anticipated for the entire transit of the aerial vehicle from theorigin to the destination in accordance with the transit plan, or maychange or vary based on the predicted location of the aerial vehicle, ora time between the departure of the aerial vehicle from the origin andan arrival of the aerial vehicle at the destination. Alternatively, oradditionally, the machine learning system may also determine aconfidence interval, a confidence level or another measure or metric ofa probability or likelihood that the predicted sound pressure levels orfrequencies will be generated or encountered by an aerial vehicle in agiven environment that is subject to given operational characteristicsat a given position.

At box 740, anti-noise intended to counteract the predicted soundpressure levels and frequencies at specified positions is determinedbased on the initial outputs. For example, where the sounds that theaerial vehicle may be expected to generate or encounter includenarrowband sound energy having specific intensities that are centeredaround discrete frequencies at a given location, anti-noise having thespecific intensities and the discrete frequencies that is one hundredeighty degrees out-of-phase with the expected sounds (or of a reversepolarity with respect to the expected sounds) may be determined. Theanti-noise may be a constant sound to be emitted at or within a vicinityof the given location in accordance with the transit plan, or mayinclude different sounds to be emitted at different times or intervalsduring the transit. In some embodiments, anti-noise need not be emittedwhere the aerial vehicle will not pass within earshot of any humans orother animals, e.g., when no such humans or animals are within avicinity of the aerial vehicle, or where distances between the aerialvehicle and such humans or animals are sufficiently large. In some otherembodiments, anti-noise need not be emitted where the expected sounds ofthe aerial vehicle are insignificant compared to ambient noise withinthe environment, e.g., where a signal-to-noise ratio is sufficientlylow, as the expected sounds of the aerial vehicle will not likely beheard. In other embodiments, the anti-noise may be intended to addressall of the sounds emitted by the aerial vehicle, while in some otherembodiments, the anti-noise may be intended to reduce the net effects ofsuch sounds to below a predetermined threshold.

At box 750, the aerial vehicle departs from the origin to thedestination, and at box 760, anti-noise is emitted at specific positionsduring the transit from the origin to the destination. For example, theaerial vehicle may monitor its position during the transit using one ormore GPS receiver or sensors and emit a discrete anti-noise, or one ormore anti-noises, at or between such specific positions during thetransit. At box 770, whether the aerial vehicle has arrived at thedestination is determined. If the aerial vehicle has arrived at thedestination, then the process ends.

If the aerial vehicle has not yet arrived at the destination, however,then the process advances to box 772, where actual operationalcharacteristics of the aerial vehicle during the transit are determined.For example, information or data regarding the actual courses or speedsof the aerial vehicle, and the operational actions, events orinstructions that caused the aerial vehicle to achieve such courses orspeeds, may be captured and recorded in at least one data store, whichmay be provided onboard the aerial vehicle, or in one or more alternateor virtual locations, e.g., in a “cloud”-based environment accessiblevia a network. At box 774, environmental conditions encountered by theaerial vehicle during the transit are determined. For example,information or data regarding the actual wind velocities, humiditylevels, temperatures, precipitation or any other environmental events orstatuses within the vicinity of the aerial vehicle may also be capturedand recorded in at least one data store.

At box 776, information or data regarding the operationalcharacteristics determined at box 772 and the environmental conditionsdetermined at box 774 are provided to the trained machine learningsystem as updated inputs, in real time or in near-real time. In someembodiments, values corresponding to the operational characteristics orenvironmental conditions are provided to the trained machine learningsystem. In some other embodiments, values corresponding to differencesor differentials between the operational characteristics determined thatwere determined at box 772 or the environmental conditions that weredetermined at box 774 and the operational characteristics that werepredicted at box 722, or the environmental conditions that werepredicted at box 724, may be provided to the trained machine learningsystem.

At box 780, predicted sound pressure levels and frequencies are receivedfrom the trained machine learning system as updated outputs. As isdiscussed above, noises that are to be generated or encountered by anaerial vehicle may be predicted in accordance with a transit plan forthe aerial vehicle, and anti-noises determined based on such predictednoises may be determined based on the transit plan, as well as any otherrelevant information or data regarding the transit plan, includingattributes of an origin, a destination or any intervening waypoints,such as locations, topography, population densities or other criteria.For example, the emission of anti-noise may be halted in order toconserve electric power on onboard sources (e.g., batteries),particularly where the predicted noises are of no consequence or wherethe anti-noise will have no measurable effect. At box 790, anti-noisesfor counteracting the predicted sound pressure levels and frequenciesreceived from the trained machine learning system based on the updatedoutputs are determined before the process returns to box 760, where suchanti-noises are emitted at specified positions.

Referring to FIGS. 8A and 8B, views of aspects of one system 800 foractive airborne noise abatement in accordance with embodiments of thepresent disclosure are shown. Except where otherwise noted, referencenumerals preceded by the number “8” shown in FIG. 8A or FIG. 8B indicatecomponents or features that are similar to components or features havingreference numerals preceded by the number “6” shown in FIG. 6, by thenumber “5” shown in FIG. 5, by the number “3” shown in FIG. 3, by thenumber “2” shown in FIG. 2 or by the number “1” shown in FIGS. 1Athrough 1D.

As is shown in FIG. 8A, a transit plan 860 of an aerial vehicle 810traveling between Manhattan, N.Y., and Flushing, N.Y., is shown. Thetransit plan 860 identifies information or data regarding an origin, adestination, and three intervening waypoints, e.g., coordinates,altitudes, courses, speeds, climb or descent rates, turn rates,accelerations, winds, humidities and temperatures. Using the transitplan 860, information or data regarding predicted noise 865 may bedetermined, e.g., based on an output received from a machine learningsystem after the transit plan 860 is provided thereto as an input.

In accordance with the present disclosure, anti-noise 865′ may beidentified based on the predicted noise 865, and emitted from the aerialvehicle 810 using one or more sound emitters. For example, as is shownin FIG. 8B, where the origin is located in an urban environment,anti-noise 865′ having amplitudes less than the amplitudes of thepredicted noise 865, and frequencies that are one hundred eighty degreesout-of-phase, may be emitted from the aerial vehicle 810 within avicinity of the origin. The anti-noise 865′ is thus intended to reduce,e.g., below an acceptable level or threshold, but not eliminate,intensities of the predicted noise 865 within a vicinity of the aerialvehicle 810 at the origin, which is characterized by the presence ofoccasionally high levels of ambient noise within the urban environment.

Conversely, where a first waypoint is located over water, anti-noise865′ need not be emitted from the aerial vehicle 810, as the aerialvehicle 810 is not expected to encounter humans or other animals thatmay be adversely affected by the emission of the predicted noise 865from the aerial vehicle 810. Where a second waypoint is located over acemetery, or other location subject to strict noise limits or thresholdsthat may be formal or informal in nature, anti-noise 865′ that is equalin amplitude to the predicted noise 865 may be emitted from the aerialvehicle 810, at a frequency that is one hundred eighty degreesout-of-phase with the frequency of the predicted noise 865.

Where a third waypoint is located within a vicinity of an internationalairport, e.g., a location having characteristically high ambient noiselevels, anti-noise 865′ need not be emitted from the aerial vehicle 810,as the predicted noise 865 within a vicinity of the third waypoint mayhave intensities that are far below the ambient noise levels, orenergies that are centered at or near frequencies that have high ambientnoise levels associated therewith. Finally, where the destination islocated within a vicinity of a sporting venue, where high intensitynoise may be commonly accepted by fans or other personnel at thesporting venue, anti-noise which slightly reduces but need notnecessarily eliminate the net effect of such noises may be emitted,thereby conserving the electrical power available onboard.

As is discussed above, a sound model provided by a trained machinelearning system may identify two or more noises having discrete soundpressure levels or intensities and frequencies that may be predicted tobe generated or encountered by an aerial vehicle during operations. Thepredicted noises may be identified in advance of the operations, or inreal time or near-real time as the operations are in progress. Inresponse, the aerial vehicle may emit two or more anti-noises in orderto counteract the effects of the predicted noises. The two or moreanti-noises may be emitted simultaneously, and at the sound pressurelevels or intensities and frequencies corresponding to the predictednoises. Alternatively, the two or more anti-noises may be emitted atsound pressure levels or intensities and frequencies according to aweighted wave superposition, e.g., such that the two or more anti-noisesmay constructively or destructively interfere with one another in apredetermined manner. In some embodiments, one of the anti-noises may bea predicted anti-noise for an aerial vehicle that is in transit, whileanother of the anti-noises may be determined in response to noises ofthe aerial vehicle that are actually observed while the aerial vehicleis in transit.

Referring to FIG. 9, a flow chart 900 of one process for active airbornenoise abatement in accordance with embodiments of the present disclosureis shown. At box 910, projected environmental conditions, operationalcharacteristics and an intended route for a transit of an aerial vehiclefrom an origin to a destination are provided to a trained machinelearning system. For example, a transit plan identifying locations of anorigin to a destination, a course and speed at which the aerial vehicleis to travel from the origin to the destination may be provided to thetrained machine learning system, along with any weather projections,ground conditions, cloud coverage, sunshine or other variables regardingthe environment at the origin and the destination, and along the coursebetween the origin and the destination.

At box 920, a predicted noise and a confidence interval for the transitare determined based on outputs received from the trained machinelearning system. The predicted noise may include a sound pressure levelor intensity (e.g., measured in decibels) and a frequency (e.g.,measured in Hertz), and any other relevant parameters. Additionally, thepredicted noise may be constant for the entire transit, or two or morepredicted noises may be identified for varying aspects of the transit,e.g., a predicted noise for when the aerial vehicle is within a vicinityof the origin, a predicted noise for when the aerial vehicle is within avicinity of the destination, and predicted noises for when the aerialvehicle is located at one or more positions (e.g., waypoints) along theroute between the origin and the destination. Similarly, the confidenceinterval may be constant for the entire transit, or may vary based onthe different aspects of the transit.

At box 930, an initial anti-noise is calculated for the transit based onthe predicted noise. The initial anti-noise may have a sound pressurelevel or intensity selected based on the sound pressure level orintensity of the predicted noise (e.g., the initial anti-noise may beintended to completely eliminate the effects of the predicted noise, orto reduce the effects of the predicted noise), and a frequency that isone hundred eighty degrees out-of-phase with the frequency of thepredicted noise.

At 940, a weighted superposition of the initial anti-noise andin-transit anti-noise is determined based on the confidence interval.For example, where the initial anti-noise may not be determined with asufficiently high degree of confidence, in-transit noises generated orencountered during a transit of an aerial vehicle may be captured andevaluated, and an anti-noise associated with those in-transit noises maybe emitted during the transit of the aerial vehicle. In someembodiments, the weighted superposition may weigh the emission of theinitial anti-noise based on the confidence interval associated with thepredicted noise. For example, where the confidence interval isseventy-five percent (75%), the weighted superposition may call forreducing the original sound pressure level or intensity of the initialanti-noise to seventy-five percent (75%) thereof, and for reducing thesound pressure level or intensity of the in-transit anti-noise totwenty-five percent (25%) thereof. In another example, where theconfidence interval is sixty percent (60%), the weighted superpositionmay call for reducing the original sound pressure level or intensity ofthe initial anti-noise to sixty percent (60%) thereof, and for reducingthe sound pressure level or intensity of the in-transit anti-noise toforty percent (40%) thereof.

At box 950, the aerial vehicle departs from the origin, and at box 960,the aerial vehicle captures in-transit noise during the transit from theorigin to the destination. For example, the aerial vehicle may includeone or more components or features for detecting and capturing soundenergy, e.g., a microphone, a piezoelectric sensor, a vibration sensor,or any other type of device, component, system, or instrument, such as atransducer, for converting acoustic energy into one or more electricalsignals. At box 970, the aerial vehicle calculates an in-transitanti-noise based on the captured in-transit noise. For example, thein-transit anti-noise may have a sound pressure level or intensity(e.g., an amplitude) that is equal to the sound pressure levels orintensities of one or more of the sounds that are captured during theflight of the aerial vehicle, and a frequency that is one hundred eightydegrees out-of-phase with the frequency of the predicted noise.

At box 980, the aerial vehicle emits the initial anti-noise calculatedat box 930 and the in-transit anti-noise determined at box 970 accordingto the weighted superposition. For example, where the weightedsuperposition calls for emitting the initial anti-noise at eightypercent (80%) of the original sound pressure level or intensity and thein-transit anti-noise at twenty percent (20%) of the original soundpressure level or intensity, the two anti-noises may be emittedaccording to such weights, and emitted simultaneously. In this regard,the quality of the initial predictions of noises that are to begenerated or encountered by the aerial vehicle may be enhanced based onin situ measurements, which may be used to calculate in-transitanti-sounds that may augment the initial anti-noise determined based onsuch initial predictions. At box 990, whether the aerial vehicle arrivesat the destination is determined, e.g., based on one or more GPSreceiver or sensors, and the process ends.

One example of the emission of anti-noise according to weightedsuperpositions is shown in FIG. 10. Referring to FIG. 10, views ofaspects of one system 1000 for active airborne noise abatement inaccordance with embodiments of the present disclosure are shown. Exceptwhere otherwise noted, reference numerals preceded by the number “10”shown in FIG. 10 indicate components or features that are similar tocomponents or features having reference numerals preceded by the number“8” shown in FIG. 8A or FIG. 8B, by the number “6” shown in FIG. 6, bythe number “5” shown in FIG. 5, by the number “3” shown in FIG. 3, bythe number “2” shown in FIG. 2 or by the number “1” shown in FIGS. 1Athrough 1D.

As is shown in FIG. 10, an aerial vehicle 1010 is en route from anorigin in Pensacola, Fla., to a destination in Orlando, Fla., along aroute that passes along the coast of the Gulf of Mexico in westernFlorida at a course of 102°, then over a portion of the Gulf of Mexicoat a course of 110°, and finally over land central Florida at a courseof 119°. The aerial vehicle 1010 emits a variety of noise 1065 while enroute to Orlando.

In accordance with the present disclosure, an initial anti-noise 1065-1′may be determined for the aerial vehicle 1010 by providing informationregarding the planned transit (e.g., a transit plan identifying theorigin, the destination and the intervening waypoints, as well aspredicted environmental conditions such as altitudes, courses, speeds,climb or descent rates, turn rates, accelerations, wind velocities,humidity levels and temperatures or operational characteristics of theaerial vehicle) to a sound model developed by a trained machine learningsystem. An output of the sound model may include information or dataregarding sounds that may be generated or encountered by the aerialvehicle 1010 during operations, and the initial anti-noise 1065-1′ maybe defined based on the output of the sound model, in terms of soundpressure levels or intensities and frequencies. For example, as is shownin FIG. 10, the initial anti-noise 1065-1′ includes a sound pressurelevel of 96 dB and a frequency of 2496 Hz for the first leg of thetransit between Pensacola and the first waypoint, a sound pressure levelof 92 dB and a frequency of 1974 Hz for the second leg of the transitbetween the first and second waypoints, and a sound pressure level of 99dB and a frequency of 2004 Hz for the third leg of the transit betweenthe second waypoint and Orlando.

Additionally, the sound model may further determine a confidenceinterval (or a confidence level or another measure or metric of aprobability or likelihood) that the output of the sound model, which wasitself determined based on extrinsic or intrinsic information or datasuch as the transit plan and any environmental conditions or operationalcharacteristics of the transit, is accurate or precise. For example, theconfidence interval may vary throughout a transit due to factors such asvarying surface conditions (e.g., differing sound reflecting orpropagating properties of sand, swamp or salt water), cloud coverage(e.g., moisture-rich clouds or dry air), winds or sunshine. As is shownin FIG. 10, the confidence interval for the first leg of the transit isseventy percent (70%), while the confidence intervals for the second andthird legs of the transit are ninety-nine percent (99%) and eightypercent (80%), respectively. Moreover, because the initial anti-noise1065-1′ is determined based on outputs of the sound model, theconfidence intervals of the outputs of the sound model may be directlycorrelated with a confidence in the initial anti-noise 1065-1′calculated based on such outputs.

In accordance with the present disclosure, anti-noise emitted duringoperation of an aerial vehicle may be based on a weighted superpositionof two or more anti-noises, such as two or more anti-noises determinedbased on two discrete predicted noises, or, in some embodiments, both aninitial anti-noise calculated based on predicted noise generated orencountered by an aerial vehicle during operations, and an in-transitanti-noise calculated based on actual noise captured by the aerialvehicle, e.g., by one or more microphones, piezoelectric sensors,vibration sensors or other transducers or sensing devices provided onthe aerial vehicle, during such operations. The relative intensities ofthe initial anti-noise and the in-transit anti-noise emitted by theaerial vehicle may be based on a weighted function that considers theconfidence in the prediction of the noise from which the initialanti-noise was determined. Thus, the weighted superposition mayincorporate the confidence interval associated with the initialanti-noise, or any other measure or metric of confidence in the initialanti-noise, or a probability or likelihood that the predicted noiseswill be generated or encountered by the aerial vehicle in transit.

Therefore, the initial anti-noise 1065-1′ may be emitted simultaneouslywith in-transit anti-noise 1065-2′ at relative ratios determined basedon a level of confidence in the accuracy and precision of the predictednoises and, therefore, the initial anti-noise 1065-1′. For example,referring again to FIG. 10, along the first leg of the transit, theinitial anti-noise 1065-1′ may be emitted at seventy percent (70%) ofits original sound pressure level or intensity, and the in-transitanti-noise 1065-2′ may be emitted at thirty percent (30%) of itsoriginal sound pressure level or intensity. Likewise, along the secondleg of the transit, the initial anti-noise 1065-1′ may be emitted atninety-nine percent (99%) of its original sound pressure level orintensity, and the in-transit anti-noise 1065-2′ may be emitted at onepercent (1%) of its original sound pressure level or intensity. Alongthe third leg of the transit, the initial anti-noise 1065-1′ may beemitted at eighty percent (80%) of its original sound pressure level orintensity, and the in-transit anti-noise 1065-2′ may be emitted attwenty percent (20%) of its original sound pressure level or intensity.

As is discussed above, anti-noise may be identified based on not onlyaspects of a transit plan (e.g., noises that may be expected at givenlocations or times, or at various altitudes, courses, speeds, climb ordescent rates, turn rates, or accelerations) but also the variouscomponents of expected noises. For example, where an aerial vehicleincludes two or more discrete sources from which the emission of noisemay be expected, anti-noises may be identified for each of such sources,or the noises emitted thereby, and the anti-noises may be emittedindependently in response to such noises.

Referring to FIG. 11, a flow chart 1100 of one process for activeairborne noise abatement in accordance with embodiments of the presentdisclosure is shown. At box 1110, a transit plan is identified for anaerial vehicle to transit from an origin to a destination. The transitplan may identify a purpose for the transit, the origin, thedestination, any intervening waypoints, or any other relevantinformation regarding the transit. At box 1120, an operating speedrequired to complete the transit in accordance with the transit plan isdetermined, and at box 1130, environmental conditions between the originand the destination during the transit are predicted. For example, theoperating speed may be calculated based on a distance between the originand the destination, an intended elapsed time for the transit, or anyoperational constraints of the aerial vehicle, as well as theenvironmental conditions predicted at box 1130, which may, in someinstances, impede or aid the aerial vehicle during the transit.

In series or in parallel, anti-noises may be identified and emitted fromthe aerial vehicle based on various elements or components of predictednoise. At box 1140A, the transit plan, the operating speed, and thepredicted environmental conditions may be provided to a first machinelearning system for predicting rotor noise. For example, the firstmachine learning system may have been trained using information or dataregarding noises associated with one or more rotors on the aerialvehicle, which may be correlated with locations, operating speeds,environmental conditions or other factors. At box 1150A, a predictedrotor noise is received from the first machine learning system, and atbox 1160A, a first anti-noise is calculated to counteract the predictedrotor noise. At box 1170A, the first anti-noise is emitted from aspeaker or, alternatively, another sound emitting device.

Similarly, at box 1140B, the transit plan, the operating speed, and thepredicted environmental conditions may be provided to a second machinelearning system for predicting motor noise, and at box 1150B, apredicted motor noise is received from the second machine learningsystem. At box 1160B, a second anti-noise is calculated to counteractthe predicted motor noise, and at box 1170B, the second anti-noise isemitted from a speaker or other sound-emitting device. Likewise, at box1140C, the transit plan, the operating speed and the predictedenvironmental conditions may be provided to a third machine learningsystem for predicting vibration noise, and at box 1150C, a predictedvibration noise is received from the third machine learning system. Atbox 1160B, a third anti-noise is calculated to counteract the predictedvibration noise, and at box 1170B, the third anti-noise is emitted froma speaker or other sound-emitting device.

At box 1180, the arrival of the aerial vehicle at the destination isdetermined. For example, a position sensor onboard the aerial vehiclemay determine that the aerial vehicle is at or near a locationassociated with the destination, e.g., to within a predeterminedtolerance. At box 1190, the first noise, the second noise and the thirdnoise are silenced, and the process ends.

An aerial vehicle may be configured to simultaneously emit variousanti-noises in parallel, and in response to noises emitted orencountered by the aerial vehicle, during operation. Referring to FIGS.12A and 12B, views of aspects of one system for active airborne noiseabatement in accordance with embodiments of the present disclosure areshown. Except where otherwise noted, reference numerals preceded by thenumber “12” shown in FIG. 12A or FIG. 12B indicate components orfeatures that are similar to components or features having referencenumerals preceded by the number “10” shown in FIG. 10, by the number “8”shown in FIG. 8A or FIG. 8B, by the number “6” shown in FIG. 6, by thenumber “5” shown in FIG. 5, by the number “3” shown in FIG. 3, by thenumber “2” shown in FIG. 2 or by the number “1” shown in FIGS. 1Athrough 1D.

As is shown in FIG. 12A, an aerial vehicle 1210 includes a plurality ofmotors 1213, a plurality of rotors 1215 and a set of onboard sensors1220, piezoelectric elements 1234 joining components of the aerialvehicle 1210 to one another, and an audio speaker 1242. As is shown inFIG. 12A and FIG. 12B, the aerial vehicle 1210 is further configured toemit anti-noise in response to noises encountered or generated duringoperations. For example, where the rotors 1215 are predicted or known toemit a first rotor noise 1265-1, a first rotor anti-noise 1265-1′ may beemitted from the audio speaker 1242. Where the rotors 1213 are predictedor known to emit a second motor noise 1265-2, a second motor anti-noise1265-2′ may be emitted from the motors 1213 themselves, e.g., from oneor more internal speakers or other sound emitting elements therein, suchas a PC speaker. Where the components of the aerial vehicle 1210 arepredicted or known to emit a third vibration noise 1265-3, a thirdvibration anti-noise 1265-3′ may be emitted from the piezoelectricelements 1234 joining the various components, e.g., by applying a chargeto a crystal provided therein and causing such elements 1234 to vibrateat a resonance frequency.

Although the disclosure has been described herein using exemplarytechniques, components, and/or processes for implementing the systemsand methods of the present disclosure, it should be understood by thoseskilled in the art that other techniques, components, and/or processesor other combinations and sequences of the techniques, components,and/or processes described herein may be used or performed that achievethe same function(s) and/or result(s) described herein and which areincluded within the scope of the present disclosure.

For example, although some of the embodiments disclosed herein referencethe use of unmanned aerial vehicles to deliver payloads from warehousesor other like facilities to customers, those of ordinary skill in thepertinent arts will recognize that the systems and methods disclosedherein are not so limited, and may be utilized in connection with anytype or form of aerial vehicle (e.g., manned or unmanned) having fixedor rotating wings for any intended industrial, commercial, recreationalor other use.

Moreover, although some of the embodiments disclosed herein depict theuse of aerial vehicles having sensors for detecting sound pressurelevels, environmental conditions, operational characteristics andpositions, and devices or components for emitting anti-noise, thesystems and methods of the present disclosure are likewise not solimited. For example, a first aerial vehicle may feature sensors fordetecting sound pressure levels, environmental conditions, operationalcharacteristics and positions, and provide information or data regardingsuch sound pressure levels, environmental conditions, operationalcharacteristics or positions to a machine learning system, which may betrained to associate such environmental conditions, operationalcharacteristics or positions with sound pressure levels. Subsequently,information or data regarding a transit of a second aerial vehicle maybe provided as an input to the machine learning system and an anti-noiseto be emitted by the second aerial vehicle may be determined based on anoutput from the machine learning system.

It should be understood that, unless otherwise explicitly or implicitlyindicated herein, any of the features, characteristics, alternatives ormodifications described regarding a particular embodiment herein mayalso be applied, used, or incorporated with any other embodimentdescribed herein, and that the drawings and detailed description of thepresent disclosure are intended to cover all modifications, equivalentsand alternatives to the various embodiments as defined by the appendedclaims. Moreover, with respect to the one or more methods or processesof the present disclosure described herein, including but not limited tothe processes represented in the flow charts of FIG. 4, 7, 9 or 11,orders in which such methods or processes are presented are not intendedto be construed as any limitation on the claimed inventions, and anynumber of the method or process steps or boxes described herein can becombined in any order and/or in parallel to implement the methods orprocesses described herein. Also, the drawings herein are not drawn toscale.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey in apermissive manner that certain embodiments could include, or have thepotential to include, but do not mandate or require, certain features,elements and/or steps. In a similar manner, terms such as “include,”“including” and “includes” are generally intended to mean “including,but not limited to.” Thus, such conditional language is not generallyintended to imply that features, elements and/or steps are in any wayrequired for one or more embodiments or that one or more embodimentsnecessarily include logic for deciding, with or without user input orprompting, whether these features, elements and/or steps are included orare to be performed in any particular embodiment.

Disjunctive language such as the phrase “at least one of X, Y, or Z,” or“at least one of X, Y and Z,” unless specifically stated otherwise, isotherwise understood with the context as used in general to present thatan item, term, etc., may be either X, Y, or Z, or any combinationthereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is notgenerally intended to, and should not, imply that certain embodimentsrequire at least one of X, at least one of Y, or at least one of Z toeach be present.

Unless otherwise explicitly stated, articles such as “a” or “an” shouldgenerally be interpreted to include one or more described items.Accordingly, phrases such as “a device configured to” are intended toinclude one or more recited devices. Such one or more recited devicescan also be collectively configured to carry out the stated recitations.For example, “a processor configured to carry out recitations A, B andC” can include a first processor configured to carry out recitation Aworking in conjunction with a second processor configured to carry outrecitations B and C.

Language of degree used herein, such as the terms “about,”“approximately,” “generally,” “nearly” or “substantially” as usedherein, represent a value, amount, or characteristic close to the statedvalue, amount, or characteristic that still performs a desired functionor achieves a desired result. For example, the terms “about,”“approximately,” “generally,” “nearly” or “substantially” may refer toan amount that is within less than 10% of, within less than 5% of,within less than 1% of, within less than 0.1% of, and within less than0.01% of the stated amount.

Although the invention has been described and illustrated with respectto illustrative embodiments thereof, the foregoing and various otheradditions and omissions may be made therein and thereto withoutdeparting from the spirit and scope of the present disclosure.

What is claimed is:
 1. An unmanned aerial vehicle (UAV) comprising: a frame; a Global Positioning System (GPS) sensor associated with the frame; a plurality of motors mounted to the frame; a plurality of propellers, wherein each of the plurality of propellers is coupled to one of the plurality of motors; a sound emitting device mounted to at least one of the frame or one of the plurality of motors; and a computing device having a memory and one or more computer processors, wherein the one or more computer processors are configured to at least: determine, by the GPS sensor, a position of the UAV; determine at least one environmental condition associated with the position; determine at least one operating characteristic of at least one of the plurality of motors or at least one of the plurality of propellers associated with the position; determine a sound pressure level of an anti-noise and a frequency of the anti-noise based at least in part on at least one of the position, the at least one environmental condition, or the at least one operating characteristic; and emit the anti-noise from the sound emitting device of the UAV.
 2. The UAV of claim 1, further comprising a microphone, and wherein the one or more computer processors are further configured to at least: capture a sound using the microphone; and identify a sound pressure level of the sound captured using the microphone and a frequency of the sound captured using the microphone, wherein the sound pressure level of the anti-noise and the frequency of the anti-noise are determined based at least in part on the sound pressure level of the sound captured using the microphone and the frequency of the sound captured using the microphone.
 3. A method to operate a first aerial vehicle comprising a first sound emitting device mounted thereto, wherein the method comprises: predicting, by at least one computer processor prior to a first time, at least one of: a first anticipated position of the first aerial vehicle at the first time; a first anticipated environmental condition at the first anticipated position or at the first time; a first anticipated operating characteristic of the first aerial vehicle at the first anticipated position or at the first time; predicting, by the at least one computer processor, a first sound to be emitted by at least one component of the first aerial vehicle at the first time, wherein the first sound is predicted based at least in part on the at least one of the first anticipated position, the first anticipated environmental condition or the first anticipated operating characteristic; determining, by the at least one computer processor, a second sound based at least in part on the first sound, wherein a second sound pressure level of the second sound is not greater than a first sound pressure level of the first sound, and wherein a second frequency of the second sound is substantially equal in magnitude and of reverse polarity with respect to a first frequency of the first sound; and causing, by the at least one computer processor, the second sound to be emitted by the first sound emitting device at the first time.
 4. The method of claim 3, wherein the second sound is caused to be emitted by the first sound emitting device at the first time.
 5. The method of claim 4, wherein the first aerial vehicle further comprises a Global Positioning System (GPS) sensor, and wherein the method further comprises: determining, by the GPS sensor, that the first aerial vehicle is at the first anticipated position at the first time, wherein the second sound is caused to be emitted by the first sound emitting device in response to determining that the first aerial vehicle is at the first anticipated position at the first time.
 6. The method of claim 3, wherein the at least one component of the first aerial vehicle is at least one of a frame of the first aerial vehicle, a motor mounted to the frame, or a propeller rotatably coupled to the motor.
 7. The method of claim 3, wherein predicting the first sound to be emitted by the at least one component of the first aerial vehicle at the first time further comprises: providing, by the at least one computer processor, first information regarding the first anticipated position, the first anticipated environmental condition and the first anticipated operating characteristic to at least one machine learning system as an input; and receiving, from the at least one machine learning system, second information regarding the first sound as an output, wherein the second information regarding the first sound comprises the first sound pressure level and the first frequency.
 8. The method of claim 7, wherein determining the second sound further comprises: providing first information regarding the first sound to at least one machine learning system as an input, wherein the information regarding the first sound comprises at least one of a first sound pressure level of the first sound or a first frequency of the first sound; and receiving, from the at least one machine learning system, second information regarding the second sound as an output, wherein the second information regarding the second sound comprises a second sound pressure level and a second frequency, wherein the second sound is caused to be emitted by the first sound emitting device at the second sound pressure level or at the second frequency.
 9. The method of claim 7, wherein the at least one machine learning system is configured to perform at least one of: an artificial neural network; a conditional random field; a cosine similarity analysis; a factorization method; a K-means clustering analysis; a latent Dirichlet allocation; a latent semantic analysis; a log likelihood similarity analysis; a nearest neighbor analysis; a support vector machine; or a topic model analysis.
 10. The method of claim 3, wherein predicting the at least one of the first anticipated position of the first aerial vehicle at the first time, the first anticipated environmental condition at the first anticipated position or at the first time, or the first anticipated operating characteristic of the first aerial vehicle at the first anticipated position or at the first time comprises: determining that a second aerial vehicle was at the first anticipated position at a second time, wherein the second time preceded the first time; and determining information regarding at least one of a second environmental condition or a second operating characteristic observed by the second aerial vehicle at the first anticipated position at the second time, wherein the first sound to be emitted by the at least one component of the first aerial vehicle at the first time is predicted based at least in part on the information regarding the at least one of the second environmental condition or the second operating characteristic.
 11. The method of claim 10, wherein the first sound to be emitted by the at least one component of the first aerial vehicle at the first time is predicted by at least one computer processor provided on the second aerial vehicle.
 12. The method of claim 3, wherein predicting the at least one of the first anticipated position of the first aerial vehicle at the first time, the first anticipated environmental condition at the first anticipated position or at the first time, or the first anticipated operating characteristic of the first aerial vehicle at the first anticipated position or at the first time comprises: generating a transit plan for the first aerial vehicle, wherein the transit plan comprises information regarding a plurality of anticipated positions of the aerial vehicle, and wherein the first anticipated position is one of the plurality of anticipated positions, wherein predicting the first sound to be emitted by the at least one component of the first aerial vehicle at the first time further comprises: predicting a plurality of sounds to be emitted by one of a plurality of components of the first aerial vehicle, wherein each of the plurality of sounds is associated with at least one of the plurality of anticipated positions of the first aerial vehicle, wherein determining the second sound further comprises: determining a plurality of anti-noises, wherein each of the plurality of anti-noises corresponds to one of the plurality of sounds, wherein each of the plurality of anti-noises has a sound pressure level not greater than a sound pressure level of the one of the plurality of sounds, wherein each of the plurality of anti-noises has a frequency that is substantially equal in magnitude and of reverse polarity with respect to a frequency of the one of the plurality of sounds, wherein the second sound is one of the plurality of anti-noises, and wherein each of the plurality of anti-noises corresponds to the one of the plurality of positions.
 13. The method of claim 3, wherein the first sound emitting device comprises one of an audio speaker, a piezoelectric sound emitter or a vibration source provided on the first aerial vehicle.
 14. The method of claim 3, further comprising: determining a noise threshold within a vicinity of the first anticipated position, wherein the second sound is determined based at least in part on the first sound and the noise threshold within the vicinity of the first anticipated position.
 15. The method of claim 14, wherein determining the second sound based at least in part on the first sound further comprises: determining at least one of the second sound pressure level or the second frequency based at least in part on the first sound and the noise threshold, wherein a sum of the first sound pressure level and the second sound pressure level is less than the noise threshold at a predetermined time.
 16. The method of claim 3, wherein the first anticipated environmental condition comprises at least one of: a first temperature at the first anticipated position or at the first time, a first barometric pressure at the first anticipated position or at the first time, a first wind speed at the first anticipated position or at the first time, a first humidity at the first anticipated position or at the first time, a first level of cloud coverage at the first anticipated position or at the first time, a first level of sunshine at the first anticipated position or at the first time, or a first surface condition at the first anticipated position or at the first time.
 17. The method of claim 3, wherein the first anticipated operational characteristic comprises at least one of: a first rotating speed of a first motor provided on the first aerial vehicle at the first anticipated position or at the first time, a first altitude of the first aerial vehicle at the first anticipated position or at the first time, a first course of the first aerial vehicle at the first anticipated position or at the first time, a first airspeed of the first aerial vehicle at the first anticipated position or at the first time, a first climb rate of the first aerial vehicle at the first anticipated position or at the first time, a first descent rate of the first aerial vehicle at the first anticipated position or at the first time, a first turn rate of the first aerial vehicle at the first anticipated position or at the first time, or a first acceleration of the first aerial vehicle at the first anticipated position or at the first time.
 18. A method comprising: identifying, by at least one computer processor, information regarding a first transit of a first aerial vehicle, wherein the first transit comprises travel over a first position by the first aerial vehicle at a first time, and wherein the information regarding the first transit of the first aerial vehicle comprises at least one of: a latitude of the first position; a longitude of the first position; an altitude of the first aerial vehicle at the first position and at the first time; a course of the first aerial vehicle at the first position and at the first time; an air speed of the first aerial vehicle at the first position and at the first time; a climb rate of the first aerial vehicle at the first position and at the first time; a descent rate of the first aerial vehicle at the first position and at the first time; a turn rate of the first aerial vehicle at the first position and at the first time; an acceleration of the first aerial vehicle at the first position and at the first time; a rotating speed of a first motor mounted to the first aerial vehicle at the first position and at the first time, wherein the first motor has a first propeller rotatably coupled thereto; or at least one frequency of at least a first sound captured by a first sound sensor provided on the first aerial vehicle at the first position and at the first time; at least one sound pressure level of at least the first sound; or a first environmental condition encountered by the first aerial vehicle at the first position and at the first time; determining, by the at least one computer processor, at least one frequency of a second sound and at least one sound pressure level of the second sound based at least in part on the information regarding the first transit of the first aerial vehicle; generating, by the at least one computer processor, a transit plan for a second transit of a second aerial vehicle, wherein the second transit comprises travel over the first position by the second aerial vehicle at a second time, wherein the transit plan comprises a plurality of instructions, and wherein one of the plurality of instructions is an instruction to emit, by a sound emitting device provided on the second aerial vehicle, at least the second sound at a second time or upon determining that the second aerial vehicle is within a vicinity of the first position, and storing the transit plan in an onboard memory of the second aerial vehicle prior to the second time.
 19. The method of claim 18, wherein determining the at least one frequency of at least the second sound and the at least one sound pressure level of at least the second sound comprises: providing, by the at least one computer processor, the information regarding the first transit of the first aerial vehicle to at least one machine learning system as an input; and receiving, from the at least one machine learning system, information regarding the second sound as an output, wherein the information regarding the second sound comprises the at least one frequency of the second sound and the at least one sound pressure level of the second sound.
 20. The method of claim 18, further comprising: identifying, by the at least one computer processor, a noise threshold within a vicinity of the first position, wherein at least one of the at least one sound pressure level of the second sound or the at least one frequency of the second sound is determined based at least in part on the noise threshold within the vicinity of the first position. 